{"id":36263,"date":"2023-03-15T19:03:37","date_gmt":"2023-03-15T18:03:37","guid":{"rendered":"https:\/\/www.statlab-unisa.it\/cladag2023\/?page_id=36263"},"modified":"2023-09-11T13:14:32","modified_gmt":"2023-09-11T11:14:32","slug":"conference-program-navigabile","status":"publish","type":"page","link":"https:\/\/www.statlab-unisa.it\/cladag2023\/conference-program-navigabile\/","title":{"rendered":"Conference program – navigabile"},"content":{"rendered":"

[et_pb_section fb_built=”1″ custom_padding_last_edited=”off|desktop” admin_label=”Hero” _builder_version=”4.17.6″ use_background_color_gradient=”on” background_color_gradient_stops=”#ffffff 0%|#e7edf9 100%” background_color_gradient_start=”#ffffff” background_color_gradient_end=”#e7edf9″ custom_margin=”|||” custom_padding=”100px||100px|” custom_padding_tablet=”130px||130px|” locked=”off” collapsed=”on” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.17.6″ header_font=”Merriweather|700|||||||” header_font_size=”46px” header_line_height=”1.3em” custom_margin=”||0px|” animation_style=”flip” animation_direction=”top” global_colors_info=”{}”]<\/p>\n

Conference Program<\/h1>\n

[\/et_pb_text][et_pb_divider color=”#4646c4″ divider_position=”center” divider_weight=”2px” _builder_version=”4.17.4″ max_width=”90px” max_width_tablet=”12%” max_width_last_edited=”off|desktop” custom_margin=”|||” animation_style=”flip” animation_delay=”50ms” global_colors_info=”{}”][\/et_pb_divider][et_pb_text disabled_on=”on|on|on” _builder_version=”4.19.0″ _module_preset=”default” animation_style=”fade” disabled=”on” global_colors_info=”{}”]<\/p>\n

COMING SOON<\/strong><\/h1>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”off|desktop” disabled_on=”on|on|on” admin_label=”hero section” _builder_version=”4.19.0″ use_background_color_gradient=”on” background_color_gradient_stops=”#ffffff 0%|#e7edf9 100%” background_color_gradient_start=”#ffffff” background_color_gradient_end=”#e7edf9″ custom_margin=”|||” custom_padding=”29px||0px|||” custom_padding_tablet=”130px||130px|” disabled=”on” collapsed=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ admin_label=”title and illustration” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_image src=”http:\/\/cladag2019.unicas.it\/wp-content\/uploads\/2018\/07\/conference_illustration_05.png” align=”right” align_tablet=”center” align_phone=”” align_last_edited=”on|desktop” _builder_version=”4.16″ height=”300px” animation_style=”slide” animation_direction=”right” animation_intensity_slide=”10%” always_center_on_mobile=”on” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.17.4″ text_font=”||||||||” header_font=”Merriweather|700|||||||” header_text_color=”#” header_font_size=”46px” header_line_height=”1.3em” custom_margin=”||0px|” animation_style=”flip” animation_direction=”top” global_colors_info=”{}”]<\/p>\n

Schedule<\/h1>\n

[\/et_pb_text][et_pb_divider color=”#4646c4″ divider_position=”center” divider_weight=”2px” _builder_version=”4.16″ max_width=”90px” max_width_tablet=”12%” max_width_last_edited=”off|desktop” custom_margin=”|||” animation_style=”flip” animation_delay=”50ms” global_colors_info=”{}”][\/et_pb_divider][et_pb_button button_url=”http:\/\/cladag2019.unicas.it\/download-files\/cladag-program-02.pdf” button_text=”Download the detailed program” button_alignment=”center” disabled_on=”on|on|on” _builder_version=”4.17.4″ custom_button=”on” disabled=”on” global_colors_info=”{}”][\/et_pb_button][et_pb_button button_url=”http:\/\/cladag2019.unicas.it\/download-files\/cladag-program-summary-scheme.pdf” button_text=”Download the summary scheme” button_alignment=”center” disabled_on=”on|on|on” _builder_version=”4.17.4″ custom_button=”on” button_text_size=”19.5px” disabled=”on” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”off|off|off” admin_label=”schedules section 11 September” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” animation_style=”zoom” animation_intensity_zoom=”10%” collapsed=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ custom_padding=”10px|0px|10px|0px” border_color_all=”#4646c4″ border_width_bottom=”3px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” header_font=”||||||||” header_2_font=”|700|||||||” header_2_line_height=”1.4em” global_colors_info=”{}”]<\/p>\n

Day 1 – Monday Sept, 11<\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Registration” _builder_version=”4.17.4″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”8:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”TBA” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Registration open<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Opening” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”9:00 – 9:30″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Opening<\/h3>\n

Vincenzo Loia<\/span>, Rector of the University of Salerno | Sergio Destefanis<\/span>, Head of the Department of Economics and Statistics, University of Salerno | Gennaro Iorio<\/span>, Head of the Department of Political and Social Studies, University of Salerno |Corrado Crocetta<\/span>, President of the Italian Statistical Society | Cinzia Viroli<\/span>, President of the CLADAG | Carla Rampichini<\/span>, Chair of CLADAG 2023 Scientific Committee<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Invited sessions #1″ _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”9:30 \u2013 11:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Invited sessions #1<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Canale” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS01 | Advances in Bayesian nonparametrics” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Antonio Canale<\/p>\n

\n

Posterior clustering for Dirichlet process mixtures of Gaussians with constant data<\/strong><\/a>
Filippo Ascolani<\/u> and Valentina Ghidini<\/p>\n

Flexible modelling of heterogeneous populations of networks: a Bayesian nonparametric approach<\/strong><\/a>
Francesco Barile, Simon\u00f3n Lunag\u00f3mez and Bernardo Nipoti<\/u><\/p>\n

Issues with sparse spatial random graphs<\/strong><\/a>
Francesca Panero<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Coretto” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS02 | Recent advances in model-based unsupervised learning” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Pietro Coretto<\/p>\n

\n

A novel multi-view ensemble clustering framework for cancer subtype discovery<\/strong><\/a>
Michael G. Schimek<\/u>, Bastian Pfeifer and Marcus D. Bloice<\/p>\n

Model-based clustering via parsimonious mixtures of dimension-wise scaled normal mixtures<\/strong><\/a>
Antonio Punzo<\/u>, Luca Bagnato and Salvatore Daniele Tomarchio<\/p>\n

Model-based clustering of right-censored lifetime data with frailties and random covariates<\/strong><\/a>
Andrea Cappozzo<\/u>, Chiara Masci, Francesca Leva and Anna Maria Paganoni<\/p>\n

Longitudinal hidden Markov models: problems and methods<\/strong><\/a>
Mackenzie R. Neal<\/u> and Paul D. McNicholas<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”off|off|off” admin_label=”Terada – Yamamoto” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-03 | Advances in large\/complex data analysis” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]Organizers: <\/span> Yoshikazu Terada and Michio Yamamoto
\nChair<\/span>:\u00a0Yoshikazu Terada<\/p>\n

\n

On some properties of reconstructed trajectories from sparse longitudinal data<\/strong><\/a>
\nYoshikazu Terada<\/p>\n

Two extensions of extended redundancy analysis for exploratory data analysis<\/strong><\/a>
\nNaoto Yamashita<\/p>\n

Clustering for sparsely sampled longitudinal data based on basis expansions<\/strong><\/a>
\nMichio Yamamoto<\/u> and Yoshikazu Terada<\/p>\n

 [\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”off|off|off” admin_label=”Grassetti” _builder_version=”4.16″ custom_padding=”0px||0px|||” collapsed=”off” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-04 | Performance estimation and players\u2019 classification: an overlook into sports analytics” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Luca Grassetti<\/p>\n

\n

Uncertainty and response style in latent trait models to assess emotional intelligence of elite swimmers<\/strong><\/a>
Rosa Fabbricatore<\/u> and Maria Iannario<\/p>\n

The Generalized Shapley measure for ranking players in basketball: applications and future directions<\/strong><\/a>
Rodolfo Metulini<\/u>, Francesco Biancalani and Giorgio Gnecco<\/p>\n

Estimation of team\u2019s strength for handball games predictions<\/strong><\/a>
Florian Felice<\/u> and Christophe Ley<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”off|off|off” admin_label=”Bertarelli” _builder_version=”4.16″ custom_padding=”0px||0px|||” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-05 | Machine learning for finite population inference” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Gaia Bertarelli<\/p>\n

\n

Flexible employment, a machine learning approach<\/strong><\/a>
Marco Alf\u00f2, Dimitris Pavlopoulos and Roberta Varriale<\/span><\/p>\n

Using ML techniques for estimation with non-probabilistic survey data<\/strong><\/a>
Jorge Rueda S\u00e1nchez<\/span>, Maria del Mar Rueda, Ram\u00f3n Ferri and Beatriz Cobo<\/p>\n

Classification tree to improve data quality in official statistics<\/strong>
Marco Di Zio, Romina Filippini, Gaia Rocchetti and Simona Toti<\/span><\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Coffee break” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”11:00 – 11:30″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Coffee break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Timing and speaker” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”11:30 – 13:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Keynote #1 – Statistics with a human face<\/h3>\n

Adrian Bowman<\/a>, University of Glasgow (SCOTLAND)<\/p>\n

[\/et_pb_text][et_pb_toggle title=”Read more…” closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Three-dimensional surface imaging, through laser-scanning or stereo-photogrammetry, provides high-resolution data defining the surface shape of objects.\u00a0\u00a0Human faces are of particular interest and there are many biological and anatomical applications, including assessing the success of facial surgery and investigating the possible developmental origins of some adult conditions.\u00a0\u00a0An initial challenge is to structure the raw images by identifying features of the face.\u00a0\u00a0Ridge and valley curves provide a very good intermediate level at which to approach this, as these provide a good compromise between informative representations of shape and simplicity of structure.\u00a0\u00a0Some of the issues involved in analysing data of this type will be discussed and illustrated.\u00a0\u00a0Modelling issues include simple comparison of groups, the measurement of asymmetry and longitudinal patterns of shape change.\u00a0\u00a0This last topic is relevant at short scale in facial animation, medium scale in individual growth patterns, and very long scale in phylogenetic studies.<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Invited sessions #2″ _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”11:30 – 13:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Invited sessions #2<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Van Deun” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-06 | Multi-view & multi-set data analysis” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Katrijn Van Deun<\/p>\n

\n

A cohort study on the gender gap in mortality through the Tucker3 model<\/strong><\/a>
Paolo Giordani, Susanna Levantesi, Andrea Nigri<\/span> and Virginia Zarulli<\/p>\n

Simultaneous clustering and variable selection on multi-view data<\/strong>
Shuai Yuan<\/p>\n

Structural equation modeling with latent\/emergent variables: RGCCAc<\/strong><\/a>
Arthur Tenenhaus<\/span>, Michel Tenenhaus and Theo Dijkstra<\/p>\n

View it differently: finding groups in microbiome data<\/strong><\/a>
Laura Anderlucci<\/span>, Silvia Dallari and Angela Montanari<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”GiordanoMisuraca” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-07 | From texts to knowledge: advances and challenges in textual data analysis” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers<\/span>:\u00a0Giuseppe Giordano and Michelangelo Misuraca
Chair<\/span>: Giuseppe Giordano<\/p>\n

\n

The nexus between ESG and initial coin offerings: evidence from text analysis<\/a> <\/strong>
Alessandro Bitetto and Paola Cerchiello<\/span><\/p>\n

Identification of misogynistic accounts on Twitter through Graph Convolutional Networks<\/a> <\/strong>
Lara Fontanella<\/span>, Emiliano del Gobbo and Alex Cucco<\/p>\n

Analysing the e\ufb00ect of di\ufb00erent design choices in network-based topic detection<\/strong><\/a>
Carla Galluccio<\/span>, Matteo Magnani, Davide Vega, Giancarlo Ragozini and Alessandra Petrucci<\/p>\n

Ensemble method for text classification in medicine with multiple rare classes<\/a> <\/strong>
Alessandro Albano<\/span>, Mariangela Sciandra and Antonella Plaia<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Ranalli” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-08 | Real Big Data applications for socio-economic phenomena” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Maria Giovanna Ranalli<\/p>\n

\n

Reducing selection bias in non-probability sample by Small Area Estimation<\/strong><\/a>
Francesco Schirripa Spagnolo, Gaia Bertarelli<\/span>, Nicola Salvati, Donato Summa, Monica Scannapieco, Stefano Marchetti and Monica Pratesi<\/p>\n

Assessing and improving data quality in open spatial data: a case study with ANAC data<\/strong><\/a>
Vincenzo Nardelli and Niccol\u00f2 Salvini<\/span><\/p>\n

Using retail transactions for consumer price index and expenditure statistics<\/strong><\/a>
Li-Chun Zhang<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Lupparelli, Galimberti” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-09 | Selected papers by the IBS (Societ\u00e0 Italiana di Biometria) – Statistical methods for the analysis of health problems” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]Organizer and Chair<\/span>: Monia Lupparelli
\nDiscussant<\/span>: Monica Chiogna<\/p>\n

\n

Clinically useful measures in survival analysis: the restricted mean survival time as an alternative to the hazard ratio<\/strong><\/a>
\nFederico Ambrogi<\/span> and Matteo Di Maso<\/p>\n

Clustering genes spatial expression profiles with the aid of external biological knowledge<\/strong><\/a>
\nAndrea Sottosanti<\/span>, Sara Agavni\u2019 Castiglioni, Stefania Pirrotta, Enrica, Calura and Davide Risso<\/p>\n

Causal inference on the impact of extreme ambient temperatures on population health<\/strong><\/a>
\nMichela Baccini<\/span>, Alessandra Mattei, Elena Degli Innocenti, Giulio Biscardi and Aitana Lertxundi<\/p>\n

 [\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Agostinelli” _builder_version=”4.16″ custom_padding=”0px||0px|||” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-10 | Machine Learning and AI” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Claudio Agostinelli<\/p>\n

\n

Depth functions for tree-indexed processes<\/strong><\/a>
Giacomo Francisci<\/span> and Anand Vidyashankar<\/p>\n

Using machine learning and AI in science of science<\/strong><\/a>
Daniele Pretolesi, Andrea Vian and Annalisa Barla<\/span><\/p>\n

Improving performance in neural networks by dendrite-activated connection<\/strong><\/a>
Carlo Metta, Marco Fantozzi, Andrea Papini, Gianluca Amato, Matteo Bergamaschi, Silvia Giulia Galfr\u00e8, Alessandro Marchetti, Michelangelo Vegli\u00f2, Maurizio Parton<\/span> and Francesco Morandin<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Lunch” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”13:00 \u2013 14:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Lunch<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Plenary Bartolucci” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”14:00 – 15:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Plenary session | Discrete latent variable models: recent advances and perspectives<\/a><\/h3>\n

Keynote speaker: Francesco Bartolucci <\/span>| Universit\u00e0 degli Studi di Perugia, Italy
Chair: Irini Moustaki<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Break” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”15:00 – 15:10″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Invited sessions #3″ _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”15:10 – 16:40″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Invited sessions #3<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Wagner” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-11 | Advances and applications in model-based clustering” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Helga Wagner<\/p>\n

\n

Modeling zone diameter measurements to infer antibiotic susceptibility of bacteria<\/strong><\/a>
Bettina Gr\u00fcn<\/span>, Thomas Petzoldt and Helga Wagner<\/p>\n

Model based clustering procedures for multivariate mixed type longitudinal data<\/strong><\/a>
Arnost Kom\u00e1rek<\/p>\n

Partial membership models for high-dimensional spectroscopy data<\/strong><\/a>
Alessandro Casa<\/span>, Thomas Brendan Murphy and Michael Fop<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Wilhelm” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-12 | Selected papers by GfKl – Data Science Society” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Adalbert F. X. Wilhelm<\/p>\n

\n

Random-based initialization for clustering mixed-type data with the k-prototypes algorithm<\/strong><\/a>
Rabea Aschenbruck<\/span>, Gero Szepannek and Adalbert F. X. Wilhelm<\/p>\n

\u201cYou call it a manifold, I call it a subspace\u201d – Selected examples on the interface between computer science and statistics in the context of clustering and manifold learning<\/strong><\/a>
Daniyal Kazempour<\/span> and Peer Kr\u00f6ger<\/p>\n

Sparse rule generating fold-change classification for molecular high-throughput profiles<\/strong><\/a>
Annika M. T. U. Kestler, Nensi Ikonomi, Silke D. Werle, Julian D. Schwab, Friedhelm Schwenker and Hans A. Kestler<\/span><\/p>\n

Constraint-based attractor search in Boolean networks using quantum computing<\/strong><\/a>
Felix M. Weidner, Mirko Rossini, Joachim Ankerhold and Hans A. Kestler<\/span><\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Battauz” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-13 | New developments in latent variable models” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Michela Battauz<\/p>\n

\n

Interpretable and accurate scaling in large-scale assessment: a variable selection approach to latent regression<\/strong><\/a>
Yunxiao Chen<\/span>, Motonori Oka and Matthias von Davier<\/p>\n

Estimation issues in multivariate panel data<\/strong><\/a>
Silvia Bianconcini and Silvia Cagnone<\/span><\/p>\n

Mid-quantile regression for discrete panel data<\/strong><\/a>
Alessio Farcomeni<\/span>, Alfonso Russo and Marco Geraci<\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Wa\u0142\u0119ga et al” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-14 | Advances in application of statistical methods in household economics” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers<\/span>: Agnieszka Wa\u0142\u0119ga, Pawe\u0142 Ulman and Barbara Pawe\u0142ek
Chair<\/span>: Pawe\u0142 Ulman<\/p>\n

\n

Inequality, populism, and unfairness: a comparison of unfair income inequalities in Poland and Norway<\/strong><\/a>
Tomasz Kwarcin\u0301ski<\/span> and Pawe\u0142 Ulman<\/p>\n

Comparison of the households\u2019 work intensity in Slovakia and Czechia through Least Squares means analysis based on GLM<\/strong><\/a>
Silvia Komara<\/span>, Martina Ko\u0161\u00edkov\u00e1, Erik \u0160olt\u00e9s and Tatiana \u0160olt\u00e9sov\u00e1<\/p>\n

Housing poverty in Europe. Multidimensional analysis<\/strong>
Pawe\u0142 Ulman,<\/span> Ma\u0142gorzata \u0106wiek and Maria Sadko<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Rocci” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-15 | Advances in clustering three-way data” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Roberto Rocci<\/p>\n

\n

A clustering model for three-way asymmetric proximity data<\/strong><\/a>
Laura Bocci<\/span> and Donatella Vicari<\/p>\n

Clustering three-way data with outliers<\/strong><\/a>
Katharine M. Clark and Paul D. McNicholas<\/span><\/p>\n

Model-based simultaneous classification and reduction for three-way ordinal data<\/strong><\/a>
Monia Ranalli<\/span> and Roberto Rocci<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Round Table” _builder_version=”4.16″ custom_margin=”|auto|16px|auto||” custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”15:10 – 16:40″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Round table: Artificial intelligence and big data in business<\/h3>\n

Organizer and moderator:\u00a0Vincenzo Esposito Vinzi<\/a>, ESSEC Business School<\/p>\n

[\/et_pb_text][et_pb_toggle title=”AI at the heart of outstanding customer experiences” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”||10px||false|false” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

\u00a0The future is now and it is all about customer experiences.\u00a0 But what does a beautiful experience look like?\u00a0 We explore how Artificial Intelligence (AI) is enabling a new era of personalization driven by deep insight and predictive optimization.\u00a0 We will look at how AI enables organizations to sense, comprehend, act and learn, and review how Accenture has put AI at the heart of its role as Innovation Partner at Carnival Corporation to transform customer experiences.<\/span><\/p>\n

[\/et_pb_toggle][et_pb_text _builder_version=”4.16″ custom_margin=”-12px||10px||false|false” custom_padding=”|||30px|false|false” global_colors_info=”{}”]Jean-Pierre Bokobza<\/span>:\u00a0Senior Managing Director \u2013 Global Geographic Sales Lead, Accenture Digital[\/et_pb_text][et_pb_toggle title=”How AI & Big Data technology are changing the customer expectation, offering new ways to offer broader and personalized experiences?” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”||10px||false|false” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Lots of industries are facing the challenges of digital transformation, customer have more choices and are now caring about experience. We will show, through real use cases, how we can leverage the advances in AI and predictive analytics to create unique offers and experiences at scale, that appeal to the needs and desires of each individual customer.<\/span><\/p>\n

[\/et_pb_toggle][et_pb_text _builder_version=”4.16″ custom_margin=”-12px||10px||false|false” custom_padding=”|||30px|false|false” global_colors_info=”{}”]Fikria Chaouki<\/span>:\u00a0Senior expert Advanced analytics & AI for business \u2013 Former Vice President of Advanced analytics & AI at Accor[\/et_pb_text][et_pb_toggle title=”Data to change: knowledge sharing between research and firms in the statistical analysis of Big data” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”||10px||false|false” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Technological evolution in the production of data does not always go hand in hand with a cultural evolution, which enables non-specialists to understand and take advantage of innovation. The cultural evolution to understand the\u00a0<\/span>key points and meaning of the profiling of the consumption behaviors, reading habits, and attitudes in real time is deeply rooted into statistics and data analysis.<\/p>\n

Data and statistical literacy have particular relevance in the activities of companies and firms, when complex analyses are undertaken by teams from diverse backgrounds.<\/p>\n

The question on how academics, statistical associations and firms can cooperate to promote and develop statistical literacy to enhance not only change and innovation, but also progress in societies has not yet a clear answer.<\/p>\n

[\/et_pb_toggle][et_pb_text _builder_version=”4.16″ custom_margin=”-12px||10px||false|false” custom_padding=”|||30px|false|false” global_colors_info=”{}”]Monica Pratesi<\/span>:\u00a0SIS President \u2013 Full Professor of Statistics, University of Pisa[\/et_pb_text][et_pb_toggle title=”How are companies using big data today? Some concrete examples.” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”||10px||false|false” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

The private industry is currently putting a lot of efforts to transform their businesses into smart businesses. In fact, the full digitalisation of companies activities\u2019 generates data usable for increasing operational efficiency and for creating new products and services. The characteristics of this data necessitates automated procedure typically using AI. Based on strategic big data analytics case studies from large companies, we give concrete examples of the type of data used, the algorithms and the data valorisation.<\/p>\n

[\/et_pb_toggle][et_pb_text _builder_version=”4.16″ custom_margin=”-12px||10px||false|false” custom_padding=”|||30px|false|false” global_colors_info=”{}”]Jeroen Rombouts<\/span>:\u00a0Head of Information Systems, Decision Sciences and Statistics Department, Accenture Strategic Business Analytics Chair – ESSEC Business School[\/et_pb_text][et_pb_toggle title=”Mobile Data for Social Good: How Mobile Operators are Building Sustainable and Responsible Business.” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”||10px||false|false” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

ICTs are an important enabler of economic growth and development. Mobile phones technology is experiencing massive penetration rates globally, even in low and middle-income locations, reaching urban and rural populations, across all socio-economic spectra. Each mobile device generates an incredible amount of information that yields unique insights on human behaviour, particularly relevant and promising for the social sector. In 2015, United Nations have called for a \u2018data revolution\u2019, as an enabler of evidence-based policies and programs to reach the most vulnerable. Mobile operators are working closely with public agencies and NGOs to establish a common framework and ecosystem that can help provide that information, to eventually improve the lives of billions of people and the environment in which they live. Orange has also supported this cause, launching several initiatives and studies that have shown the value of engaging through and about data.<\/span><\/p>\n

[\/et_pb_toggle][et_pb_text _builder_version=”4.16″ custom_margin=”-12px||10px||false|false” custom_padding=”|||30px|false|false” global_colors_info=”{}”]<\/p>\n

Stefania Rubrichi<\/span>:\u00a0Research scientist \u2013 ORANGE\u2019s SENSE (Sociology and Economics of Networks and Services) Laboratory<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Coffee break” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”16:40 \u2013 17:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Coffee break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Contributed sessions #1″ _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”17:00 – 18:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Contributed sessions #1<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS01″ _builder_version=”4.16″ min_height=”32px” custom_padding=”0px||1px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-01 | Statistical methods for educational data” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0 Michela Battauz<\/p>\n

\n

Propensity towards Master’s degree: choices of northern students after BAs?<\/strong><\/a>
Giuseppe Alfonzetti<\/span>, Luca Grassetti and Laura Rizzi<\/p>\n

When nonresponse makes estimates from a census small area estimation problem: the case of the survey on graduates’ Employment Status in Italy<\/strong><\/a>
Maria Giovanna Ranalli<\/span>, Fulvia Pennoni, Francesco Bartolucci and Antonietta Mira<\/p>\n

Classifying northern Italian students in their transition to Master degree<\/strong><\/a>
Giuseppe Alfonzetti, Luca Grassetti<\/span> and Laura Rizzi<\/p>\n

A competing risk analysis of academic careers with students’ ability and speed as predictors<\/strong><\/a>
Michela Battauz<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS02″ _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-02 | Statistical modelling I” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Helga Wagner<\/p>\n

\n

Matrix-variate hidden Markov regressions<\/strong><\/a>
Salvatore D. Tomarchio<\/span>, Antonio Punzo and Antonello Maruotti<\/p>\n

Multilevel cross-classified latent class models<\/strong><\/a>
Silvia Columbu,<\/span> Nicola Piras and Jeroen K. Vermunt<\/p>\n

Hidden Markov models for multivariate longitudinal data<\/strong><\/a>
Alexa Sochaniwsky<\/span> and Paul D. McNicholas<\/p>\n

Shrinkage of time-varying effects in panel data models<\/strong><\/a>
Helga Wagner<\/span> and Roman Pfeiler<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS03″ _builder_version=”4.16″ custom_padding=”0px||6px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-03 | Clustering I” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Francesca Martella<\/p>\n

\n

Cluster analysis and conditional copula: a joint approach to analyse energy demand<\/strong><\/a>
Marta Di Lascio and Roberta Pappad\u00e0<\/span><\/p>\n

Capturing correlated clusters using mixtures of latent class models<\/strong><\/a>
Geltraud Malsiner-Walli<\/span>, Bettina Gr\u00fcn and Sylvia Fr\u00fchwirth-Schnatter<\/p>\n

The multivariate cluster-weighted disjoint factor analyzers model<\/strong><\/a>
Francesca Martella<\/span>, Xiaoke Qin, Wangshu Tu and Sanjena Subedi<\/p>\n

 <\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS04″ _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-04 | Bayesian analysis I” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Beatrice Franzolini<\/p>\n

\n

Bayesian forecasting of multivariate longitudinal zero-inflated counts: an application to civil conflict<\/strong><\/a>
Beatrice Franzolini<\/span>, Laura Bondi, Augusto Fasano and Giovanni Rebaudo<\/p>\n

Bayesian aggregation of crowd judgments for quantitative fact checking<\/strong><\/a>
Michele Lambardi di San Miniato<\/span>, Michela Battauz, Ruggero Bellio and Paolo Vidoni<\/p>\n

Bayesian analysis for a graphical t-model<\/strong><\/a>
Andriette Bekker, Johan T. Ferreira<\/span>, J. Pillay and M. Arashi<\/p>\n

Group’s heterogeneity in rating tasks: a Bayesian semi-parametric approach<\/strong><\/a>
Giuseppe Mignemi<\/span>, Joanna Manolopoulou and Antonio Calcagn\u00ec<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS05″ _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-05 | Statistical methods for socio-economic data I” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Christian Usala<\/p>\n

\n

Building improved gender equality composite indicators by object-oriented Bayesian networks<\/strong><\/a>
Lorenzo Giammei<\/span>, Flaminia Musella, Fulvia Mecatti and Paola Vicard<\/p>\n

On model-based clustering for equitable and sustainable well-being at local level: how many Italies?<\/strong><\/a>
Natalia Golini<\/span>, Francesca Martella and Antonello Maruotti<\/p>\n

Is the subjective financial well-being of Polish families changing with time? An empirical study based on constrained latent Markov models<\/strong><\/a>
Ewa Genge<\/p>\n

Inequalities at entrance, labour market conditions and university dropout: first evidence from Italy<\/strong><\/a>
Cristian Usala<\/span>, Isabella Sulis and Mariano Porcu<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Timing and speaker” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”19:20″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

MLT Concert<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Networking Event” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”19:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

\u200bA networking event with music, food, and drink (link<\/a>)<\/h3>\n

Church of Saint George & Temple of Pomona (Salerno)
<\/strong><\/p>\n

(included in the conference fees)<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”off|off|off” admin_label=”schedules section 12 September” _builder_version=”4.19.0″ background_color=”#f5f5f5″ custom_padding=”0px||0px|||” animation_style=”zoom” animation_intensity_zoom=”10%” collapsed=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.19.0″ custom_padding=”10px|0px|10px|0px” border_color_all=”#4646c4″ border_width_bottom=”3px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” header_font=”||||||||” header_2_font=”|700|||||||” header_2_line_height=”1.4em” global_colors_info=”{}”]<\/p>\n

Day 2 – Tuesday Sept, 12<\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Contributed sessions #2″ _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”8:50 \u2013 9:50″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Contributed sessions #2<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS6″ _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-06 | Statistical learning for geoscience and energy data” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Germana Scepi<\/p>\n

\n

Linear random forest to predict energy consumption<\/strong><\/a>
Gianpaolo Zammarchi<\/p>\n

Hierarchical percentile clustering to analyse greenhouse gas emissions from agriculture in European Union<\/strong><\/a>
Marta Di Lascio, Fabrizio Durante and Aurora Gatto<\/span><\/p>\n

Efficient disentangling \u03b3-ray sources from diffuse background in the sky map<\/strong><\/a>
Francesco Freni<\/span> and Giovanna Menardi<\/p>\n

Spatial modelling of pyroclastic cover deposit thickness with remote sensing data and ground measurements: a forecasting combination approach<\/strong><\/a>
Raffaele Mattera, Germana Scepi<\/span>, Pooria Ebrahimi and Fabio Matano<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS7″ _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-07 | Functional data analysis and quality control” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Tonio Di Battista<\/p>\n

\n

Method for the quality control and operators training in maintenance activities<\/strong><\/a>
Massimiliano Giacalone, Vincenzo Dottorini, Giuseppe Oddo, Vito Santarcangelo and Angelo Romano<\/span><\/p>\n

Supervised classification of curves by functional data analysis: an application to neuromarketing data<\/strong><\/a>
Salvatore Latora<\/span> and Luigi Augugliaro<\/p>\n

Functional data analysis approach for identifying redundancy in air quality monitoring stations<\/strong><\/a>
Annalina Sarra, Adelia Evangelista<\/span>, Tonio Di Battista and Sergio Palermi<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS8″ _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-08 | Statistical embedding” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Giuseppe Bove<\/p>\n

\n

Stratified sampling on data nuggets: a strategy for data reduction<\/strong><\/a>
Ravi Kumar Gangadharan<\/span>, Vanessa Petrarca, Maria Chiara Pagliarella, Giovanni C. Porzio<\/p>\n

A three-way \u201cindirect\u201d redundancy analysis<\/strong><\/a>
Laura Marcis<\/span>, Maria Chiara Pagliarella and Renato Salvatore<\/p>\n

An application of asymmetric multidimensional scaling to the VQR 2015-2019 data<\/strong><\/a>
Giuseppe Bove<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS9″ _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-09 | Machine learning” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>: Paolo Pagnottoni<\/p>\n

\n

Fuzzy ensemble machine learning algorithm to improve prediction<\/strong>
Nicol\u00f2 Biasetton<\/span>, Riccardo Ceccato, Marta Disegna and Alberto Molena<\/p>\n

Explainable machine learning for lending default classification<\/strong><\/a>
Golnoosh Babaei, Paolo Pagnottoni<\/span> and Thanh Thuy Do<\/p>\n

AutoSynth index: a synthetic indicator for socio-economic development based on autoencoders<\/strong><\/a>
Giulio Grossi<\/span> and Emilia Rocco<\/p>\n

A support vector machine approach to create oblique decision trees for regression<\/strong><\/a>
Andrea Carta<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS10″ _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-10 | Network data analysis” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Fulvia Pennoni<\/p>\n

\n

Multi-level stochastic blockmodels for multiplex networks<\/strong><\/a>
Maria Francesca Marino<\/span>, Matteo Sani and Monia Lupparelli<\/p>\n

A compositional stochastic block model for the analysis of the Erasmus programme network<\/strong><\/a>
Iuliia Promskaia<\/span>, Adrian O\u2019Hagan and Michael Fop<\/p>\n

Visualization of proximity and role-based embedding in a regional labour flow network<\/strong><\/a>
Sara Geremia<\/span>, Fabio Morea and Domenico De Stefano<\/p>\n

Improving clustering in temporal networks through an evolutionary algorithm<\/strong><\/a>
Luca Brusa<\/span> and Fulvia Pennoni<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS11″ _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 6″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-11 | Nonparametric inference and resampling” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Domenico Vistocco<\/p>\n

\n

Adoption of 4.0 technologies and related obstacles. Application of a multivariate nonparametric test for categorical variables<\/strong><\/a>
Stefano Bonnini and Michela Borghesi<\/span><\/p>\n

A multivariate permutation test for association<\/strong><\/a>
Elena Barzizza, Riccardo Ceccato<\/span>, Solomon Harrar, Fortunato Pesarin and Luigi Salmaso<\/p>\n

Resampling for stability estimation vs. cluster validation via data splitting and subsampling. Which approach is better in detection of clusters in taxonomy?<\/strong><\/a>
Dorota Rozmus<\/p>\n

DEndrogram Slicing through a PermutatiOn Test Approach reconsidered<\/strong><\/a>
Lucio Palazzo<\/span>, Alfonso Iodice D\u2019Enza, Francesco Palumbo and Domenico Vistocco<\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Break” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”9:50 – 10:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Plenary Claeskens” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”10:00 – 11:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Plenary session | Selective inference after variable selection by the randomized group lasso method<\/a><\/h3>\n

Keynote speaker: Gerda Claeskens<\/span>| Katholieke Universiteit Leuven, Belgium
Chair: Maria Giovanna Ranalli<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Coffee break” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”11:00 \u2013 11:30″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Coffee break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Invited sessions #4″ _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”11:30 \u2013 13:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Invited sessions #4<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Niang” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-16 | Selected papers by the SFC – Soci\u00e9t\u00e9 Fran\u00e7aise de Classification” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Nd\u00e8ye Niang<\/p>\n

\n

Distances, orders and spaces<\/a> <\/strong>
Pascal Pr\u00e9a<\/p>\n

Clustering longitudinal ordinal data<\/a> <\/strong>
Julien Jacques<\/span> and Francesco Amato<\/p>\n

Multiple imputation for clustering on incomplete data<\/a> <\/strong>
Vincent Audigier<\/span> and Nd\u00e8ye Niang<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”off|off|off” admin_label=”Hubert” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-17 | Anomaly detection” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Mia Hubert<\/p>\n

\n

The cellwise Minimum Covariance Determinant estimator<\/strong><\/a>
Jakob Raymaekers<\/span> and Peter J. Rousseeuw<\/p>\n

Visualizing anomalies in circular data<\/strong><\/a>
Davide Buttarazzi and Giovanni C. Porzio<\/span><\/p>\n

A proposal for the joint automated detection of clusters and anomalies<\/strong><\/a>
Luis A. Garc\u00eda-Escudero<\/span>, Christian Hennig, Agust\u00edn Mayo-Iscar, Gianluca Morelli and Marco Riani<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”off|off|off” admin_label=”Rampichini” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-18 | Latent variable models for complex data structures” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Carla Rampichini<\/p>\n

\n

DIF analysis with unknown groups and anchor item<\/strong>s<\/a>
Gabriel Wallin, Yunxiao Chen and Irini Moustaki<\/span><\/p>\n

A two-component Markov switching regression model<\/strong><\/a>
Roberto Colombi and Sabrina Giordano<\/span><\/p>\n

Test equating with evolving latent ability<\/strong><\/a>
Silvia Bacci,<\/span> Bruno Bertaccini, Carla Galluccio, Leonardo Grilli and Carla Rampichini<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”off|off|off” admin_label=”Cavicchia” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-19 | Advances in clustering and dimensionality reduction” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]Organizer <\/span>: Carlo Cavicchia
\n Chair<\/span>: Michel van de Velden<\/p>\n

\n

Ultrametric Gaussian Mixture models with parsimonious structures<\/strong><\/a>
\nGiorgia Zaccaria<\/p>\n

Clusterpath Gaussian graphical modeling<\/strong><\/a>
\nDaniel J. W. Touw<\/span>, Patrick J. F. Groenen, Ines Wilms and Andreas Alfons<\/p>\n

Improved interpretation methods for joint multidimensional scaling and cluster analysis with external information<\/strong>
\nMichel van de Velden<\/span>, Carlo Cavicchia and Maurizio Vichi<\/p>\n

 [\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Vitale, Giordano” _builder_version=”4.16″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-20 | Advanced clustering methods for complex networks” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers<\/span>: Maria Prosperina Vitale and Giuseppe Giordano
Chair<\/span>: Maria Prosperina Vitale<\/p>\n

\n

Cluster analysis for the study of online visual communication<\/strong><\/a>
Matteo Magnani<\/span>, Matias Piqueras, Alexandra Segerberg, Davide Vega and Victoria Yantseva<\/p>\n

Mobility across crimes: statistically validated networks and temporal pattern recognition<\/strong><\/a>
Vincenzo Genova<\/span>, C. Edling, H. Mondani, A. M. Rostami and M. Tumminello<\/p>\n

Cluster analysis of cancer metabolic network ensembles<\/strong><\/a>
Ichcha Manipur, Ilaria Granata, Lucia Maddalena and Mario R. Guarracino<\/span><\/p>\n

Evaluation of the performance of a modularity-based consensus community detection algorithm<\/strong><\/a>
Fabio Morea and Domenico De Stefano<\/span><\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Lunch” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”13:00 \u2013 14:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Lunch<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Plenary Kneib” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”14:00 \u2013 15:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Plenary session | Rage against the mean – an introduction to distributional regression<\/a><\/h3>\n

Keynote speaker: Thomas Kneib <\/span>| Georg-August-Universit\u00e4t G\u00f6ttingen, Germany
Chair: Helga Wagner<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle title=”Read more…” open_toggle_background_color=”#f5f5f5″ closed_toggle_background_color=”#F5F5F5″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ disabled_on=”on|on|on” _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” disabled=”on” locked=”off” global_colors_info=”{}”]To exploit better the structure of the rich sets of characteristics, such as clinical biomarkers, molecular profiles or detailed ontology records, that are currently being collected on large samples of healthy or diseased individuals, statistical models of the variations within and the interplay between different layers of data can be constructed. Generic Bayesian model building strategies and algorithms have been tailored for this purpose. In this talk, I will discuss three areas: implementing joint hierarchical modelling of a large number of responses and a large number of features to discover features associated with many responses ; analysing tree structured ontology data with application for finding the underlying genetic origin of rare diseases; and characterising network structures using fast Bayesian inference in large Gaussian graphical models. Common statistical issues of accounting for model uncertainty, ability to borrow information for retaining power and scalability of Bayesian computations will be highlighted. Modelling strategies and computations will be illustrated on case studies.<\/span>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Break” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”15:00 – 15:10″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Invited session #5″ _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”15:10 – 16:40″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Invited sessions #5<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Agostinelli” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-21 | Robust procedures” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Claudio Agostinelli<\/p>\n

\n

Efficiency and robustness in supervised learning<\/a> <\/strong>
Anand Vidyashankar<\/span>, Fengnan Deng, Giacomo Francisci and Xiaoran Jiang<\/p>\n

Outlier explanation based on Shapley values for vector- and matrix-valued observations<\/a> <\/strong>
Peter Filzmoser<\/span> and Marcus Mayrhofer<\/p>\n

Trimmed kernel mean shift<\/a> <\/strong>
Luca Greco<\/span>, Giovanna Menardi and Marco Rudelli<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”LaRoccaGrilli” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-22 | eXplainable Artificial Intelligence” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers<\/span>:\u00a0Leonardo Grilli and Michele La Rocca
Chair<\/span>:\u00a0Leonardo Grilli<\/p>\n

\n

From accuracy to robustness of AI systems <\/strong>
Paolo Giudici and Emanuela Raffinetti<\/span><\/p>\n

Explainable machine learning for bag of words-based phishing detection
<\/strong> Maria Carla Calzarossa, Paolo Giudici and Rasha Zieni<\/p>\n

Network interference and effect modification<\/strong><\/a>
Falco J. Bargagli-Stoffi, Costanza Tort\u00fa<\/span> and Laura Forastiere<\/p>\n

Optimal and robust combination of forecasts via constrained optimization and shrinkage<\/a> <\/strong>
Fr\u00e9d\u00e9ric Vrins<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”on|on|on” admin_label=”Lula, Pawelek” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-22 | Statistical learning methods in finance and business” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers <\/span>:\u00a0Pawe\u0142 Lula and Barbara Pawe\u0142ek
Chair <\/span>:\u00a0Barbara Pawe\u0142ek<\/p>\n

\n

Corporate bankruptcy prediction: application of statistical learning methods<\/strong>
Barbara Pawe\u0142ek and Maria Sadko<\/p>\n

Deep neural network in the modeling of the dependence structure in risk aggregation<\/strong>
Anna Denkowska, Krystian Szcz\u0119sny, Joao Vieito and Stanis\u0142aw Wanat<\/p>\n

The comparative analysis of publication activity in Hungary and Poland in the field of economics, finance and business<\/strong>
Pawe\u0142 Lula, Zsuzsanna G\u00e9ring, Magdalena Talaga, Ildik\u00f3 D\u00e9n-Nagy and R\u00e9ka Tam\u00e1ssy<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Bakk” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-23 | Measurement uncertainty in complex models” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Zsuzsa Bakk<\/p>\n

\n

An R package for multilevel latent class analysis with covariates<\/strong><\/a>
Johan Lyrvall<\/span>, Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser and Jouni Kuha<\/p>\n

Three-step rectangular latent Markov modeling based on ML correction<\/strong><\/a>
Rosa Fabbricatore<\/span>, Roberto Di Mari, Zsuzsa Bakk, Mark de Rooij and Francesco Palumbo<\/p>\n

Trimmed factorial k-means<\/strong><\/a>
Matteo Farn\u00e8<\/p>\n

Measurement invariance testing of latent class models using residual statistics and likelihood ratio test<\/strong><\/a>
Zsuzsa Bakk<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Vantini, Montagna” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-24 | Functional and object-oriented data analysis” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers <\/span>: Simone Vantini and Silvia Montagna
Chair <\/span>: Simone Vantini<\/p>\n

\n

Clustering imbalanced functional data<\/strong><\/a>
Michelle Carey<\/span> and Catherine Higgins<\/p>\n

Nonparametric local inference for functional data defined on manifold domains<\/strong><\/a>
Niels Lundtorp Olsen, Alessia Pini<\/span> and Simone Vantini<\/p>\n

Sparse clustering for functional data<\/a> <\/strong>
Fabio Centofanti,<\/span> Antonio Lepore and Biagio Palumbo<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Pennoni” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-25 | Latent variable and hidden Markov models for Big Data Analytics” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Fulvia Pennoni<\/p>\n

\n

How to build your latent Markov model: the role of time and space<\/a> <\/strong>
Roland Langrock<\/span> and Sina Mews<\/p>\n

Tree-based regression within a hidden Markov model framework<\/a>
<\/strong> Rouven Michels<\/span>, Timo Adam and Marius \u00d6tting<\/p>\n

Case-control variational inference for large scale stochastic block models<\/a>
<\/strong>Silvia Pandolfi<\/span> and Francesco Bartolucci<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”FensorePanzera” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 6″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-26 | Advances in directional statistics” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers<\/span>: Stefania Fensore and Agnese Panzera
Chair<\/span>:\u00a0Agnese Panzera<\/p>\n

\n

Data-driven smoothing parameter selection for circular data analysis<\/a> <\/strong>
Jose Ameijeiras-Alonso<\/p>\n

Circular regression with measurement errors<\/a>
<\/strong> Marco Di Marzio<\/span>, Chiara Passamonti and Charles Taylor<\/p>\n

Segmenting toroidal time series by nonhomogeneous hidden semi-Markov models<\/a>
<\/strong>Francesco Lagona<\/span> and Marco Mingione<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”CoffeBreak” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”16:40 – 17:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Coffee break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Contributed session #3″ _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”17:00 – 18:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Contributed sessions #3<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS12″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-12 | Classification methods” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Simona Balzano<\/p>\n

\n

Real-time discriminant analysis in the presence of label and measurement noise <\/strong><\/a>
Mia Hubert<\/span>, Iwein Vranckx, Jakob Raymaekers, Bart De Ketelaere and Peter Rousseeuw<\/p>\n

A supervised classification strategy based on the novel directional distribution depth function <\/strong><\/a>
Edoardo Redivo<\/span> and Cinzia Viroli<\/p>\n

Comparing soft classification methods for the rare type match problem<\/strong><\/a>
Giulia Cereda<\/span>, Fabio Corradi and Cecilia Viscardi<\/p>\n

Visualizing classification results: graphical tools for DD-classifiers<\/strong><\/a>
Houyem Demni<\/span> and Simona Balzano<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS13″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-13 | Statistical inference” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>: Marcella Niglio<\/p>\n

\n

A statistical test to assess the non-normality of the latent variable distribution<\/strong><\/a>
Lucia Guastadisegni<\/span>, Irini Moustaki, Silvia Cagnone and Vassilis Vasdekis<\/p>\n

Complete records over independent FGM sequences<\/strong><\/a>
Amir Khorrami Chokami<\/p>\n

Goodness\u2013of\u2013fit test for single functional index model<\/strong><\/a>
Lax Chan<\/span> and Aldo Goia<\/p>\n

Maximum likelihood approach to parameter selection in the spectral clustering algorithm<\/strong><\/a>
Cinzia Di Nuzzo<\/span> and Salvatore Ingrassia<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS14″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-14 | Clustering II” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Christian Henning<\/p>\n

\n

Modal clustering for categorical data<\/strong><\/a>
Noemi Corsini<\/span> and Giovanna Menardi<\/p>\n

A proposal of deep fuzzy clustering by means of the simultaneous approach<\/strong><\/a>
Claudia Rampichini<\/span> and Maria Brigida Ferraro<\/p>\n

A method to validate clustering partitions<\/strong><\/a>
Luca Frigau<\/span>, Giulia Contu, Marco Ortu and Andrea Carta<\/p>\n

Quantifying variable importance in cluster analysis<\/strong><\/a>
Christian Hennig<\/span> and Keefe Murphy<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS15″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-15 | Statistical methods for high-dimensional and positional data” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Ursula Laa<\/p>\n

\n

Modelling soccer players field position via mixture of Gaussians with flexible weights<\/strong><\/a>
Marco Berrettini<\/span>, Giuliano Galimberti, Thomas Brendan Murphy and Saverio Ranciati<\/p>\n

New tour methods for visualizing high-dimensional data<\/strong><\/a>
Ursula Laa<\/span> and Dianne Cook<\/p>\n

A new alternative method for simultaneous feature selection and determination of influential data points in Cox model with high dimensional dataset<\/strong><\/a>
Nuriye Sancar<\/span> and Deniz Inan<\/p>\n

Robust penalized multivariate analysis for high-dimensional data<\/strong><\/a>
Pia Pfeiffer<\/span> and Peter Filzmoser<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS16″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-16 | Bayesian analysis II” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Andrea Tancredi<\/p>\n

\n

One-inflated Bayesian mixtures for population size estimation <\/strong><\/a>
Davide Di Cecco, Andrea Tancredi<\/span> and Tiziana Tuoto<\/p>\n

Personalized treatment selection model for survival outcomes <\/strong><\/a>
Matteo Pedone<\/span>, Raffaele Argiento and Francesco C. Stingo<\/p>\n

A Bayesian spatio-temporal regression approach for confounding adjustment <\/strong><\/a>
Carlo Zaccardi,<\/span> Pasquale Valentini and Luigi Ippoliti<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS17″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 6″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-17 | Time series analysis I” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>: Domenico Piccolo<\/p>\n

\n

Customer satisfaction and motivation in film tourism: analysis of textual TripAdvisor data <\/strong><\/a>
Rosa Arboretti, Elena Barzizza, Nicol\u00f2 Biasetton<\/span> and Marta Disegna<\/p>\n

Markov switching autoregressive models for the analysis of hydrological time series<\/strong><\/a>
Roberta Paroli and Luigi Spezia<\/span><\/p>\n

Classification of daily streamflow data: a study on regime changes <\/strong><\/a>
Marcella Corduas<\/span> and Domenico Piccolo<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”on|on|on” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”Machine learning and data mining” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Machine learning models for forecasting stock trends<\/b>
Giacomo Camba<\/span><\/mark>\u00a0(Universit\u00e0 di Cagliari, ITALY) – Claudio Conversano<\/span>\u00a0(Universit\u00e0 di Cagliari, ITALY)<\/p>\n

Mixtures of experts with flexible concomitant covariate effects: a Bayesian solution<\/b>
Marco Berrettini<\/span><\/mark>\u00a0(Universit\u00e0 di Bologna, ITALY) – Giuliano Galimberti<\/span>\u00a0(Universit\u00e0 di Bologna, ITALY) – Thomas Brendan Murphy<\/span>\u00a0(University College Dublin, IRELAND) – Saverio Ranciati<\/span> (Universit\u00e0 di Bologna, ITALY)<\/p>\n

Comparing tree kernels performances in argumentative evidence classification<\/b>
Davide Liga<\/span><\/mark>\u00a0(Universit\u00e0 di Bologna, ITALY)<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Timing and speaker” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”14:30 \u2013 15:30″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Keynote #3 – Unifying Data Units and Models in (Co-)Clustering<\/h3>\n

Christophe Biernacki<\/a>, Universit\u00e9 Lille 1 (FRANCE)[\/et_pb_text][et_pb_toggle title=”Read more…” closed_toggle_background_color=”#F5F5F5″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]Statisticians are already aware that any modelling process issue (exploration, prediction) is wholly data unit dependent, to the extend that it should be impossible to provide a statistical outcome without specifying the couple (unit,model). In this talk, this general principle is formalized with a particular focus in model-based clustering and co-clustering in the case of possibly mixed data types (continuous and\/or categorical and\/or counting features), being also the opportunity to revisit what the related data units are. Such a formalization allows to raise three important spots: (i) the couple (unit,model) is not identifiable so that different interpretations unit\/model of the same whole modelling process are always possible; (ii) combining different \u201cclassical\u201d units with different \u201cclassical\u201d models should be an interesting opportunity for a cheap, wide and meaningful enlarging of the whole modelling process family designed by the couple (unit,model); (iii) if necessary, this couple, up to the non identifiability property, could be selected by any traditional model selection criterion. Some experiments on real data sets illustrate in detail practical beneficits from the previous three spots. It is a joint work with Alexandre Lourme (University of Bordeaux).<\/span>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Timing and speaker” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”15:30 \u2013 15:45″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Coffee break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Timing and speaker” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”15:45 \u2013 17:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Invited sessions #6<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”on|on|on” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”Latent variable models for complex networks – II” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers<\/span>:\u00a0Marco Alf\u00f2 (Universit\u00e0 di Roma \u201cLa Sapienza\u201d, ITALY) – Francesco Bartolucci (Universit\u00e0 di Perugia, ITALY)
Chair<\/span>: Daniel Sewell (University of Iowa, US)<\/p>\n

\n

Joining factorial methods and blockmodeling for the analysis of affiliation networks<\/b>
Daniela D’Ambrosio<\/span>\u00a0(Universit\u00e0 di Napoli Federico II, ITALY)<\/span> – Marco Serino<\/span>\u00a0(INVALSI, ITALY)<\/span> – Giancarlo Ragozini<\/span><\/mark>\u00a0(Universit\u00e0 di Napoli Federico II, ITALY)<\/span><\/p>\n

Dynamic clustering of network data: a hybrid maximum likelihood approach<\/b>
Maria Francesca Marino<\/span><\/mark>\u00a0(Universit\u00e0 di Firenze, ITALY)<\/span> – Maria Silvia Pandolfi<\/span>\u00a0(Universit\u00e0 di Perugia, ITALY)<\/p>\n

A dynamic stochastic block model for longitudinal networks<\/b>
Catherine Matias<\/span><\/mark>\u00a0(French National Centre for Scientific Research, FRANCE) <\/span> – Tabea Rebafka<\/span>\u00a0(Sorbonne Universit\u00e9, FRANCE) – Fanny Villers<\/span>\u00a0(Sorbonne Universit\u00e9, FRANCE)<\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”on|on|on” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”Methodologies for mixture modeling” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Francesca Greselin (Universit\u00e0 Milano Bicocca, ITALY)<\/p>\n

\n

A stochastic blockmodel for network interaction lengths over continuous time<\/b>
Michael Fop<\/span><\/mark>\u00a0(University College Dublin, IRELAND)<\/span> – Riccardo Rastelli<\/span>\u00a0(University College Dublin, IRELAND)<\/span><\/p>\n

Mixture modelling with skew-symmetric component distributions<\/b>
Geoffrey McLachlan<\/span><\/mark>\u00a0(University of Queensland, AUSTRALIA)<\/span><\/p>\n

A fast and efficient modal EM algorithm for Gaussian mixtures<\/b>
Luca Scrucca<\/span><\/mark>\u00a0(Universit\u00e0 di Perugia, ITALY) <\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”on|on|on” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”Modern likelihood methods” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer<\/span>:\u00a0Cristiano Varin (Universit\u00e0 Ca\u2019 Foscari Venezia, ITALY)
Chair<\/span>: Michele La Rocca (Universit\u00e0 di Salerno, ITALY)<\/p>\n

\n

Modern likelihood-frequentist inference at work<\/b>
Ruggero Bellio<\/span><\/mark>\u00a0(Universit\u00e0 di Udine, ITALY)<\/span> – Donald A. Pierce<\/span>\u00a0(Oregon State University, US)<\/span><\/p>\n

Bias reduction for estimating functions and pseudolikelihoods<\/b>
Nicola Lunardon<\/span><\/mark>\u00a0(Universit\u00e0 Milano Bicocca, ITALY)<\/span><\/p>\n

Computationally efficient inference for latent position network models<\/b>
Riccardo Rastelli<\/span><\/mark>\u00a0(University College Dublin, IRELAND)<\/span> – Florian Maire<\/span>\u00a0(University of Montreal, CANADA) – Nial Friel<\/span>\u00a0(University College Dublin, IRELAND)<\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”on|on|on” _builder_version=”4.19.0″ min_height=”32px” custom_padding=”0px||1px|||” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”Statistical challenges in functional data analysis” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Tonio di Battista (Universit\u00e0 \u201cG. d\u2019Annunzio\u201d Chieti \u2013 Pescara, ITALY)<\/p>\n

\n

Functional approaches for satellite data clustering and fusion<\/b>
Claire Miller<\/span><\/mark>\u00a0(University of Glasgow, UK) – Marian Scott<\/span>\u00a0(University of Glasgow, UK) – Craig Wilkie<\/span>\u00a0(University of Glasgow, UK) – Ruth O’Donnell<\/span>\u00a0(University of Glasgow, UK) – Mengyi Gong<\/span>\u00a0(British Geological Survey, UK) – Anna Sehn<\/span>\u00a0(University of Glasgow, UK)<\/p>\n

Assessing social interest in burnout using functional data analysis through google trends<\/b>
Ana M. Aguilera<\/span>\u00a0(Universit\u00e0 di Torino, ITALY) – Francesca Fortuna<\/span><\/mark>\u00a0(Universit\u00e0 di Torino, ITALY) – Manuel Escabias<\/span>\u00a0(Universit\u00e0 di Torino, ITALY)<\/p>\n

Functional data analysis for spatial aggregated point patterns in seismic science<\/b>
Elvira Romano<\/span><\/mark>\u00a0(Universit\u00e0 della Campania Luigi Vanvitelli, ITALY) <\/span> – Jonatan A. Gonz\u00e1lez<\/span>\u00a0(University Jaume I, SPAIN) – Francisco J. Rodr\u00edguez Cort\u00e9s<\/span>\u00a0(National University of Colombia, COLOMBIA) – Jorge Mateu<\/span>\u00a0(University Jaume I, SPAIN)<\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Timing and speaker” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”17:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Shuttle bus to the Abbey<\/h3>\n

(guided tour included in the social dinner fees)<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Timing and speaker” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”20:30″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||5px||false|false” global_colors_info=”{}”]<\/p>\n

Social dinner<\/h3>\n

[\/et_pb_text][et_pb_blurb title=”Mignano Montelungo Castle” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”off|off|off” admin_label=”Cladag Assembly” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”18:00 \u2013 18:45″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

ClaDAG Assembly<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”off|off|off” admin_label=”PhD Best Paper” _builder_version=”4.16″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”18:45 \u2013 19:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

PhD Student\/Young Researcher Best Paper Award<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”SocialDinner” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”20:30″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Social Dinner (link<\/a>)<\/h3>\n

Diocesan Museum (Salerno)
<\/strong><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”off|off|off” admin_label=”schedules section 13 September” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” animation_style=”zoom” animation_intensity_zoom=”10%” collapsed=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ custom_padding=”10px|0px|10px|0px” border_color_all=”#4646c4″ border_width_bottom=”3px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” header_font=”||||||||” header_2_font=”|700|||||||” header_2_line_height=”1.4em” global_colors_info=”{}”]<\/p>\n

Day 3 – Wednesday Sept, 13<\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Contributed session #4″ _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”8:50 – 9:50″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Contributed sessions #4<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS18″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-18 | Markov models and quantile regression” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Cristina Davino<\/p>\n

\n

Inference on the state distribution in periodic hidden Markov models <\/strong><\/a>
Carlina C. Feldmann<\/span>, Sina Mews, Rouven Michels and Roland Langrock<\/p>\n

Measurement invariance: a method based on latent Markov models <\/strong><\/a>
Francesco Dotto<\/span>, Roberto Di Mari, Alessio Farcomeni and Antonio Punzo<\/p>\n

A comparison between the varying-thresholds model and quantile regression <\/strong><\/a>
Niccol\u00f2 Ducci, Leonardo Grilli and Marta Pittavino<\/span><\/p>\n

The use of principal components in quantile regression: a simulation study <\/strong><\/a>
Cristina Davino, Tormod N\u00e6s, Rosaria Romano and Domenico Vistocco<\/span><\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS19″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-19 | Statistical methods for official and administrative data” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Flora Fullone<\/p>\n

\n

An application of CART algorithm to administrative data: analysis of youth initial employment trajectories <\/strong><\/a>
Ilaria Rocco<\/p>\n

Analysis of the need for working timber starting from Istat industrial production data <\/strong><\/a>
Flora Fullone,<\/span> Gianmarco Farina, Enza Compagnone, Mirella Morrone and Gioacchino de Candia<\/p>\n

A case study of electronic medical records use for predicting kidney injury <\/strong><\/a>
Davide Passaro<\/span>, Luca Tardella, Giovanna Jona Lasinio, Tiziana Fragasso, Valeria Raggi and Zaccaria Ricci<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS20″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-20 | Clustering and regression trees” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Marcella Niglio<\/p>\n

\n

Multivariate regression tree to investigate the Italian mortality rates <\/strong><\/a>
Giulia Contu<\/span>, Luca Frigau, Marco Ortu and Sara Pau<\/p>\n

One-dimensional mixture-based clustering for ordinal responses <\/strong><\/a>
Kemmawadee Preedalikit, Daniel Fern\u00e1ndez, Ivy Liu, Louise McMillan, Marta Nai Ruscone<\/span> and Roy Costilla<\/p>\n

A proposal to evaluate the solution of a fuzzy clustering algorithm <\/strong><\/a>
Carmela Iorio<\/span>, Giuseppe Pandolfo and Antonio D\u2019Ambrosio<\/p>\n

Model-based clustering for torus data <\/strong><\/a>
Luca Greco, Antonio Lucadamo<\/span> and Claudio Agostinelli<\/p>\n

 <\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS21″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-21 | Statistical learning” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Rosaria Simone<\/p>\n

\n

A flexible topic model <\/strong><\/a>
Roberto Ascari<\/span> and Alice Giampino<\/p>\n

Fuzzy functions with elastic-net estimators based on possibilistic FCM <\/strong>
Nihat Tak<\/p>\n

Residuals diagnostics for model-based trees for ordered rating responses <\/strong><\/a>
Rosaria Simone<\/p>\n

Multivariate regression tree topic modeling <\/strong><\/a>
Marco Ortu<\/span>, Giulia Contu and Luca Frigau<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS22″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-22 | Statistical methods for socio-economic data II” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Antonella Bianchino<\/p>\n

\n

How women react to their partners\u2019 work instability. The added-worker effect <\/strong><\/a>
Donata Favaro and Anna Giraldo<\/span><\/p>\n

A comparative study of financial literacy using data from PISA survey <\/strong><\/a>
Sabrina Giordano, Roberta Varriale<\/span> and Mariangela Zenga<\/p>\n

An interdisciplinary methodology for socio-economic segregation analysis <\/strong><\/a>
Antonio De Falco<\/span> and Antonio Irpino<\/p>\n

Tourism as support in the economic development of inner areas: a multi-sources approach <\/strong><\/a>
Antonella Bianchino, Daniela Fusco<\/span>, Paola Giordano, Maria Antonietta Liguori, Maria Carmina Palma and Donato Summa<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Break” _builder_version=”4.19.0″ _module_preset=”default” custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” collapsed=”off” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.19.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb title=”9:50 – 10:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.19.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Plenary session Olhede” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”10:00 – 11:00″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Plenary session | On Graph Limits as Models for Interaction Data<\/a><\/h3>\n

Keynote speaker: Sofia Charlotta Olhede <\/span>| \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Switzerland
Chair: Mario Rosario Guarracino<\/p>\n

[\/et_pb_text][et_pb_toggle title=”Read more…” closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ disabled_on=”on|on|on” _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” disabled=”on” locked=”off” global_colors_info=”{}”]TBA[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Coffee break” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”11:00 – 11:20″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Coffee break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Invited sessions #7″ _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”11:20 – 12:50″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Invited sessions #7<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Montagna” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-27 | Advances in Bayesian Factor Analysis” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Silvia Montagna<\/p>\n

\n

Integrative factor models for biomedical applications<\/a> <\/strong>
Alejandra Avalos-Pacheco<\/span> and Roberta De Vito<\/p>\n

Normalized latent measure factor models<\/a> <\/strong>
Mario Beraha<\/span> and Jim E. Griffin<\/p>\n

Latent Bayesian clustering for topic modelling<\/strong><\/a>
Lorenzo Schiavon<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Lula, Pawelek” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-28 | Statistical learning methods in finance and business” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers <\/span>:\u00a0Pawe\u0142 Lula and Barbara Pawe\u0142ek
Chair <\/span>: Pawe\u0142 Lula<\/p>\n

\n

Corporate bankruptcy prediction: application of statistical learning methods<\/strong><\/a>
Barbara Pawe\u0142ek and Maria Sadko<\/span><\/p>\n

Deep neural network in the modeling of the dependence structure in risk aggregation<\/strong><\/a>
Anna Denkowska<\/span>, Krystian Szcz\u0119sny, Joao Vieito and Stanis\u0142aw Wanat<\/p>\n

The comparative analysis of publication activity in Hungary and Poland in the field of economics, finance and business<\/strong><\/a>
Pawe\u0142 Lula<\/span>, Zsuzsanna G\u00e9ring, Magdalena Talaga, Ildik\u00f3 D\u00e9n-Nagy and R\u00e9ka Tam\u00e1ssy<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ disabled_on=”on|on|on” admin_label=”LaRoccaGrilli” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-28 | eXplainable Artificial Intelligence” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers<\/span>:\u00a0Leonardo Grilli and Michele La Rocca
Chair<\/span>:\u00a0Leonardo Grilli<\/p>\n

\n

From accuracy to robustness of AI systems <\/strong>
Paolo Giudici and Emanuela Raffinetti<\/p>\n

Explainable machine learning for bag of words-based phishing detection
<\/strong> Maria Carla Calzarossa, Paolo Giudici and Rasha Zieni<\/p>\n

Network interference and effect modification<\/strong>
Falco J. Bargagli-Stoffi, Costanza Tort\u00fa and Laura Forastiere<\/p>\n

Optimal and robust combination of forecasts via constrained optimization and shrinkage <\/strong>
Fr\u00e9d\u00e9ric Vrins<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”Guarracino” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-29 | Networks and higher-order networks data analysis and applications” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizer and Chair<\/span>: Mario Rosario Guarracino<\/p>\n

\n

Cluster analysis for networks using a fuzzy approach<\/a> <\/strong>
Ilaria Bombelli<\/span>, Ichcha Manipur and Maria Brigida Ferraro<\/p>\n

The broad phenotype-specific applications of the network-based SWIM tool<\/a> <\/strong>
Federica Conte<\/span> and Paola Paci<\/p>\n

Testing graph clusterability: a density based statistical test for directed graphs<\/a> <\/strong>
Houyem Demni<\/span>, Pierre Miasnikof, Alexander Y. Shestopaloff, Cristi\u00e1n Bravo and Yuri Lawryshyn<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”BritoDias” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-30 | Selected papers by CLAD – Recent advances in symbolic data analysis” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers<\/span>:\u00a0Paula Brito and Jos\u00e9 G. Dias
Chair<\/span>:\u00a0Jos\u00e9 G. Dias<\/p>\n

\n

Multiclass classification of distributional data<\/a> <\/strong>
Ana Santos, S\u00f3nia Dias<\/span>, Paula Brito and Paula Amaral<\/p>\n

Visualizing interval Fisher Discriminant Analysis results<\/strong><\/a>
M. Ros\u00e1rio Oliveira<\/span>, Diogo Pinheiro and Lina Oliveira<\/p>\n

Sparse and robust estimators for outlier detection in distributional data <\/strong><\/a>
Pedro Duarte Silva<\/span>, Peter Filzmoser and Paula Brito<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”ConversanonDAmbrosio” _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”IS-31 | Preference Data Analysis” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Organizers<\/span>:\u00a0Claudio Conversano and Antonio D\u2019Ambrosio
Chair<\/span>:\u00a0Claudio Conversano
Discussant<\/span>:\u00a0Antonio D\u2019Ambrosio<\/p>\n

\n

Scoring distances between equivalence and preference relations<\/a> <\/strong>
Boris Mirkin<\/p>\n

Distance-based aggregation and consensus for preference-approvals <\/strong><\/a>
Alessandro Albano<\/span>, Mariangela Sciandra and Antonella Plaia<\/p>\n

A new accurate heuristic algorithm to solve the rank aggregation problem with a large number of objects <\/strong><\/a>
Maurizio Romano<\/span> and Roberta Siciliano<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Lunch” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”12:50 – 13:40″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Lunch<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Timing and speaker” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”10:55 \u2013 11:55″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Keynote #4 – Bayesian Model-Based Clustering with Flexible and Sparse Priors<\/h3>\n

Bettina Gruen<\/a>, Johannes Kepler Universitat Linz (AUSTRIA)[\/et_pb_text][et_pb_toggle title=”Read more…” closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]Finite mixtures are a standard tool for clustering observations. However,
\nselecting the suitable number of clusters, identifying cluster-relevant
\nvariables as well as accounting for non-normal shapes of the clusters are
\nstill challenging issues in applications. Within a Bayesian framework we
\nindicate how suitable prior choices can help to solve these issues. We
\nachieve this considering mainly prior distributions that have the
\ncharacteristics that they are conditionally conjugate or can be
\nreformulated as hierarchical priors, thus allowing for simple estimation
\nusing MCMC methods with data augmentation.<\/span>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”on|on|on” admin_label=”Timing and speaker” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”10:55 \u2013 11:55″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Keynote #4 – Bayesian Model-Based Clustering with Flexible and Sparse Priors<\/h3>\n

Bettina Gruen<\/a>, Johannes Kepler Universitat Linz (AUSTRIA)[\/et_pb_text][et_pb_toggle title=”Read more…” closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.16″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]Finite mixtures are a standard tool for clustering observations. However,
\nselecting the suitable number of clusters, identifying cluster-relevant
\nvariables as well as accounting for non-normal shapes of the clusters are
\nstill challenging issues in applications. Within a Bayesian framework we
\nindicate how suitable prior choices can help to solve these issues. We
\nachieve this considering mainly prior distributions that have the
\ncharacteristics that they are conditionally conjugate or can be
\nreformulated as hierarchical priors, thus allowing for simple estimation
\nusing MCMC methods with data augmentation.<\/span>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Contributed Session #5″ _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”13:40 – 14:25″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Contributed sessions #5<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS23″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 1″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.16″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-23 | Time series analysis II” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>:\u00a0Paola Cerchiello<\/p>\n

\n

Granger network on Santa Maria del Fiore Dome<\/strong><\/a>
Fiammetta Menchetti<\/p>\n

A testing approach to detect COVID-19 waves<\/strong>
Arianna Agosto<\/span> and Paola Cerchiello<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS24″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 2″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-24 | Statistical modelling II” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>: Francesco Palumbo<\/p>\n

\n

Efficient computation of predictive probabilities in probit models via expectation propagation <\/strong><\/a>
Augusto Fasano<\/span>, Niccol\u00f2 Anceschi, Beatrice Franzolini and Giovanni Rebaudo<\/p>\n

Lattice of Gaussian graphical models for paired data with common undirected structure <\/strong><\/a>
Dung Ngoc Nguyen<\/span> and Alberto Roverato<\/p>\n

Finite mixture models: a systematic review <\/strong><\/a>
Jos\u00e9 G. Dias<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS25″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 3″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-25 | GLM and survival models” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>: Marialuisa Restaino<\/p>\n

\n

Student mobility in higher education: a destination-specific local analysis <\/strong><\/a>
Luca Scaffidi Domianello<\/p>\n

Detecting the positions of nonconsensus amino acids in HIV patients by marginal likelihood thresholding <\/strong><\/a>
Claudia Di Caterina<\/p>\n

Variable ranking in bivariate copula survival models <\/strong><\/a>
Danilo Petti<\/span>, Marcella Niglio and Marialuisa Restaino<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS26″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 4″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-26 | Panel and compositional data” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>: Luigi Spezia<\/p>\n

\n

Testing clusters of locations in spatial dynamic panel data models <\/strong><\/a>
Giuseppe Feo<\/span>, Francesco Giordano, Marcella Niglio, Sara Milito and Maria Lucia Parrella<\/p>\n

Structural zeros in regression models with compositional explanatory variables <\/strong><\/a>
Francesco Porro<\/p>\n

Assessing the degree of competitiveness among countries in the EU using Eurostat indicators<\/a> <\/strong>
Paolo Mariani, Andrea Marletta, Piero Quatto and Mariangela Zenga<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ admin_label=”CS27″ _builder_version=”4.19.0″ custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Room 5″ use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_toggle title=”CS-27 | Clustering III” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” link_option_url_new_window=”on” border_width_all=”0px” locked=”off” global_colors_info=”{}”]<\/p>\n

Chair<\/span>: Maria Lucia Parrella<\/p>\n

\n

A clustering method for distributional data based on a LDQ transformation <\/strong><\/a>
Rosanna Verde, Gianmarco Borrata<\/span> and Antonio Balzanella<\/p>\n

A Stata implementation of cluster weighted models: the CWMGLM package<\/strong><\/a>
Daniele Spinelli<\/span>, Salvatore Ingrassia and Giorgio Vittadini<\/p>\n

K-means clustering – new variations <\/strong><\/a>
Andrzej Soko\u0142owski, Ma\u0142gorzata Markowska<\/span> and Maciej Laburda<\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Break” _builder_version=”4.19.0″ _module_preset=”default” custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.19.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb title=”14:25 – 14:30″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.19.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”PlenaryGreselin” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”14:30 – 15:30″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Plenary session | To get the best, tame the beast: robust ML estimation for mixture models<\/a><\/h3>\n

Keynote speaker: Francesca Greselin <\/span>|Universit\u00e0 degli Studi Milano-Bicocca, Italy
Chair: Cinzia Viroli<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle title=”Read more…” closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ open_icon_color=”#4646c4″ disabled_on=”on|on|on” _builder_version=”4.19.0″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” disabled=”on” locked=”off” global_colors_info=”{}”]<\/p>\n

Massive amounts of data used to make quicker, better and more intelligent decisions to create business value are nowadays available for companies and organizations. Terms like big data, data science, analytics, artificial intelligence, machine learning etc., are very common in both academia and industry. All these areas of research are orientated towards answering the increasing demand for understanding trends and\/or discovering patterns in data. Usually, collected data is massive and uncertain due to noise, incompleteness and inconsistency. The main goal of a statistician\/data scientist is therefore to turn massive data into feasible information, the latter intended as able to describe efficiently an observed phenomenon, to gain indications about its future evolution as well as to provide useful insights for the ongoing decisional process. All these considerations lead towards arguing that the role of the statistician\/data scientist considerably evolved in the latest years.<\/p>\n

In my presentation, after a brief description of the scenario summarized above, I will discuss three examples\/case studies concerning image validation, hotels\u2019 reputation and social media popularity trying to give a contribution to the debate about turning the enormous amount of available data into feasible statistics. In all cases, ad-hoc but standard classification methods are used to obtain information that is extremely feasible and adds value to a decisional process.
\n<\/span>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Closing” _builder_version=”4.19.0″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”15:30 – 15:45″ use_icon=”on” font_icon=”}||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Main Hall” use_icon=”on” font_icon=”||divi||400″ icon_color=”#a9aab7″ icon_placement=”left” image_icon_width=”24px” _builder_version=”4.19.0″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||” icon_font_size=”24px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.19.0″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|” global_colors_info=”{}”]<\/p>\n

Closing and farewell<\/h3>\n

Carla Rampichini<\/span>, Chair Scientific Committee | Michele La Rocca<\/span>, Chair Local Organizing Committee<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

Conference ProgramCOMING SOONSchedule Day 1 – Monday Sept, 11Registration openOpening Vincenzo Loia, Rector of the University of Salerno | Sergio Destefanis, Head of the Department of Economics and Statistics, University of Salerno | Gennaro Iorio, Head of the Department of Political and Social Studies, University of Salerno |Corrado Crocetta, President of the Italian Statistical Society […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-36263","page","type-page","status-publish","hentry"],"yoast_head":"\nConference program - navigabile - Cladag2023<\/title>\n<meta name=\"description\" content=\"Cladag 2023 program\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.statlab-unisa.it\/cladag2023\/conference-program-navigabile\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Conference program - 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