Invited Sessions

Advanced Clustering Methods for Complex Social Networks

In many real-world applications, complex systems play a key role. Complexity and Network Analysis share an even more common language and intrinsic features, as systems containing collections of many interacting “objects”.
The focus of the session is to capture the inherent complexity in recent application fields by using network analysis methods and models. Complex networks go beyond the simple directed-undirected and weighted-unweighted data structures described in one-mode or two-mode networks. Multilayer, multilevel and multimode networks are an example of such kinds of complex structures, allowing to represent a multitude of scenarios including different types of relationships, actors, and dynamic snapshots. The network data can themselves be enriched with additional features, such as attribute variables and meta-data, with the aim of describing real phenomena in more detail. Within this scenario, advanced clustering methods and community detection algorithms should be proposed to capture the complexity of network data structures in several application fields and in everyday life.

Organizer: Maria Prosperina Vitale,  University of Salerno, ITALY – Giuseppe Giordano, University of Salerno, ITALY

5

TBA

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Advances and Applications in model based clustering

Model based clustering relies on the specification of finite mixture models to modell heterogeneity and detecting clusters in data.Starting from finite mixtures of univariate Normal distributions the field has rapidly evolved. Currently interest is in complex models, like mixtures of nonlinear regression models, mixtures of longitudinal and functional data or mixed continous and discrete data, and modelling issues like selection of the number of components or estimation strategies that avoid overfitting.

Organizer: Helga Wagner, JKU Institute of Applied Statistics, AUSTRIA

5

Bettina Grün

Vienna University of Economics and Business
AUSTRIA
5

Arnost Komarek

Charles University Prague
CZECH REPUBLIC

5

Alessandro Casa

Free University of Bozen-Bolzano
ITALY

Advances in application of statistical methods in household economics

Increasing economic and social inequalities have recently become a challenge. Building a modern policy, diagnosing and solving social problems requires the use of various statistical methods. This session aims to be a response to the demand for better explanations of changing households’ positions in recent years. The goal of this session is to explore state-of-art developments in the application of statistical methods in household economics. We would like to focus on the classification and analysis of the distribution of household consumption, saving, wealth and indebtedness. Theoretical and applied issues of well-being, financial fragility, poverty, labour participation, and the economics of marriage are also welcome.

Organizers: Agnieszka Wałęga, Cracow University of Economics, POLAND – Barbara Pawełek, Cracow University of Economics, POLAND

5

Agnieszka Wałega et al.

Cracow University of Economics
POLAND

 

5

TBA

5

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Advances in Bayesian Factor Analysis

Factor analysis is a popular method to obtain a sparse representation of the covariance matrix of multivariate observations. This is particularly relevant for analysing the correlation among high-dimensional data. Factor models have achieved large popularity in many applied areas, such as genetics, economics, and finance. The goal of this session is to review recent research developments in the area of Bayesian factor analysis. Topics include, but are not limited to, models’ identifiability, estimation of factor loadings, sparsity, selection of factor dimensionality, efficient posterior computation, theoretical developments, as well as novel applications.

Organizer: Silvia Montagna, University of Torino, ITALY

5

TBA

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Advances in Bayesian Nonparametrics

TBA

Organizer: Antonio Canale, University of Padova, ITALY

5

Bernardo Nipoti

University of Milano Bicocca
ITALY

5

TBA

5

Filippo Ascolani

University of Milano Bocconi
ITALY

Advances in Clustering and dimensionality reduction

Dimensionality reduction refers to a set of techniques used in statistics and machine learning to map high-dimensional data into a low-dimensional space in order to find and visualize the main relationships characterizing the data. Cluster analysis is the grouping of objects such that objects in the same cluster are more similar to each other than they are to objects in another cluster. Both techniques have wide applicability and are ofter used in combination to project the data into a lower dimensional space to visualize the data and find patterns. This session thus aims at defining the state-of-art of their literature.

Organizer: Carlo Cavicchia, Erasmus University Rotterdam, The Netherlands

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Advances in clustering three-way data

Multivariate data take a three-way form when we observe a set of variables on the same units in different occasions. Such data are also called matrix-variate because we observe a variables x occasions data matrix for each unit. Models to analyze three-way data are quite sophisticated because of the complex structure of the data. However, since three-way data emerge in different fields, such as psychology, economics, sociology, biology, chemistry etc., practitioners often request such kinds of models which can exploit the richness of the information contained in the data. The aim of this session is to present some of the recent advances in the area of three-way analysis with particular reference to models for the unsupervised classification of the units.

Organizer: Roberto Rocci, Sapienza University of Roma, ITALY

5

Laura Bocci

Sapienza University of Rome
ITALY

5

Paul McNicholas

McMaster University
CANADA
5

Monia Ranalli

Sapienza University of Rome
ITALY

Advances in directional statistics

TBA

Organizers: Stefania Fensore, Università “G. d’Annunzio” Chieti – Pescara, ITALY – Agnese Panzera, Università di Firenza, ITALY

5

Rosa Maria Crujeiras Casais

University of Santiago de Compostela
SPAIN
5

Marco Di Marzio

University of Chieti-Pescara
ITALY
5

Arthur Pewsey

University of Extremadura
SPAIN

Advances in Large/Complex Data Analysis

Recently we can easily get a large amount of data in various application fields. On the other hand, such data usually contains many outliers and would be very noisy. Moreover, the obtained data commonly has a more complex structure than classical multivariate data. Thus, new developments of robust estimation, complex data analysis (such as functional data analysis), and computational reduction are required in many fields. In addition, we reaffirm the importance of exploratory data analysis. Therefore, this session will introduce new developments in complex data analysis and computational reduction techniques.

Organizers: Yoshikazu Terada, Osaka University, JAPAN – Michio Yamamoto, Osaka University, JAPAN

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Anomaly detection

Detection of outliers in different types of data

Organizer: Mia Hubert, KU Leuven, BELGIUM

5

Jakob Raymaekers

University of Maastricht
The Netherlands

5

Giovanni Porzio

University of Cassino
ITALY

5

Luis Angel Garcia Escudero

University of Valladolid
SPAIN

From texts to knowledge: advances and challenges in textual data analysis

In a digitalised world, hectic and complex in many different facets, textual data became a fundamental source of knowledge. In several situations, people, firms and public institutions produce and exchange – on a daily basis – opinions, reports, enquiries in the form of written communications. Differently from classic numeric and categorial datasets, these raw texts need a careful pre-process before being quantitatively analysed, because the underlying information is difficult to be retrieved since natural language is inherently unstructured from a data analysis point of view. For this reason, it is necessary to implement a multi-stage process able to distil from a text collection a set of structured data that can be analysed with statistical methods. Text Mining encompasses different tasks that can satisfy many informative needs, from text summarisation to information extraction and organisation. In this framework, during the last years, scholars have adapted well known multivariate methods to the analysis of textual data or developed innovative approaches specifically focused on textual data. This specialised session aims at discussing the most recent developments in this research domain, offering to the CLADAG audience an overview about the advances concerning the algebraic models for text representation, the approaches for reducing dataset dimensionality, the categorisation of texts, both considering an unsupervised as well as a supervised standpoint.

Organizers: Giuseppe Giordano, University of Salerno, ITALY – Michelangelo Misuraca, University of Calabria, ITALY

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Functional and Object-oriented Data Analysis

With the advance of modern technology, more and more data are being recorded over a continuous domain resulting in functional data. Although the underlying functions are often continuous and smooth, the observed data are discrete and noisy measurements. Typical goals of FDA are function estimation (smoothing), multiple comparison of curves, functional clustering, dependence structure learning or functional regression, where the response and/or the covariates are functional. The goal of this section is to review novel contributions in FDA.

Organizers: Simone Vantini, Politecnico of Milano, ITALY – Silvia Montagna, University of Torino, ITALY

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Latent variable and hidden Markov models for big data analytics

TBA

Organizer: Fulvia Pennoni, University of Milano Bicocca, ITALY

5

Roland Langrock

Bielefeld University
GERMANY
5

Christophe Ambroise

University of Evry val d’Essone
FRANCE
5

Silvia Pandolfi

University of Perugia
ITALY

Latent variable models for complex data structures

TBA

Organizer: Silvia Bacci, University of Firenze, ITALY

5

Irini Moustaki

London School of Economics
UNITED KINGDOM

5

Sabrina Giordano et al.

University of Calabria
ITALY

5

Silvia Bacci et al.

University of Florence
ITALY

 

Machine Learning and AI

TBA

Organizer: Claudio Agostinelli, University of Trento, ITALY

5

Giacomo Francisci

George Mason University
USA

5

Annalisa Barla

University of Genova
ITALY

5

Maurizio Parton

University of Chieti – Pescara
ITALY

Machine learning for finite population inference

Inference for fintie populations is essentially a prediction problem. In this sense, Machine learning methods are proving particularly useful in exploiting information coming from low-cost auxiliary data sources such as Administrative registers and non-probability surveys. The session aims at exploring theoretical and applied issues. The list of tentative speakers involve researchers from the Academia and National Statistical Institutes.

Organizer: Gaia Bertarelli, Sant’Anna School of Advanced Studies, ITALY

5

Roberta Varriale

Istat
ITALY

MARCO ALFO’

Sapienza University of Rome
ITALY

5

Maria del Mar Rueda et al.

Universidad de Granada
SPAIN

5

Francesca Chiaromonte et al.

Sant’Anna School of Advanced Studies
ITALY

Measurement uncertainty in complex models

TBA

Organizer: Zsuzsa Bakk, Leiden University, The Netherlands

5

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Multi-view data analysis

TBA

Organizer: Katrijn Van Deun, Tilburg University, The Netherlands

5

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Networks and higher-order networks data analysis and applications

TBA

Organizer: Mario Guarracino, University of Cassino, ITALY

5

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New developments in latent variable models

Latent variable models are particularly suitable to account for the dependencies between observations. The complexity of the data often requires the development of new modelling approaches that involve a large number of parameters, which are difficult to estimate using traditional methods. This session aims at presenting recent developments in model formulation and estimation, as for example composite likelihoods and regularization.

Organizer: Michela Battauz, University of Udine, ITALY

5

Yunxiao Chen

London School of Economics and Political Science
United Kingdom

5

Silvia Cagnone

University of Bologna
ITALY

5

Alessio Farcomeni

University of Rome “Tor Vergata”
ITALY

Nonparametric Inference and Prediction

TBA

Organizer: Simone Vantini, Politecnico of Milano, ITALY

5

TBA

5

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5

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Performance estimation and players’ classification: An overlook into sports analytics

Evaluation and classification of individuals in sports have a central role in Sports Analytics literature. Team managers and coaches are learning that the statistical analysis of sports data can be crucial for effective evidence-based decision-making. Model-based methods, machine learning approaches and classical index-based studies can be used to analyse sports data. Moreover, the growing availability of disaggregated datasets (also including big data frameworks such as those collected by sensors on F1 race cars) represents a stimulus for developing new, scalable, and flexible methods. This section aims at collecting recent contributions to this field of study.

Organizer: Luca Grassetti, University of Udine, ITALY

5

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Preference Data Analysis

TBA

Organizers: Claudio Conversano, University of Cagliari ITALY – Antonio D’Ambrosio, University of Napoli Federico II, ITALY

5

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Real Big Data applications for socio-economic applications

Dan Ariely provocatively said that Big data is like teenage sex 🙂 In this session we would like to bring together real big data applications for prediction problems. The works of the tentative speakers cover the use of big data (mostly coming from web-scraping and sacnner) for estimation of economic indicators and businesses parameters of interest.

Organizer: Maria Giovanna Ranalli, University of Perugia, ITALY

5

Gaia Bertarelli et al.

Sant’Anna School of Advanced Studies
ITALY

 

5

Niccolò Salvini et al.

Catholic University Rome
ITALY

 

5

Li-Chun Zhang

University of Southampton
United Kingdom

 

Recent advances in model-based unsupervised learning

TBA

Organizer: Pietro Coretto, University of Salerno, ITALY

5

TBA

5

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5

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Robust procedures

TBA

Organizer: Claudio Agostinelli, University of Trento, ITALY

5

Anand N. Vidyashankar

George Mason University
USA

5

Peter Filzmoser

TU Wien
AUSTRIA

5

Luca Greco

University Giustino Fortunato
ITALY

Selected papers by CLAD - Associação Portuguesa de Classificação e Análise de Dados

TBA

Organizers: Paola Brito, Universidade do Porto, PORTUGAL – José G. Dias, Instituto Universitário de Lisboa ISCTE-IUL, PORTUGAL

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Selected papers by GfKl - Data Science Society

Organizer: Adalbert F.X. Wilhelm, Jacobs University Bremen, GERMANY

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Selected papers by the IBS - Statistical methods for the analysis of health problems

This session is organized by the Italian Region of the International Biometric Society with the intent to encourage the discussion on the use of statistical methods for the analysis of complex structure of data related to health problems. The development of advanced methodologies is indeed required to face the increasing proliferation of data and the new challenges which continuously rise in the clinical, omics and environmental domains. The session is composed of three invited speakers and a discussant specialized on the statistical analysis of health data.

Organizers: Monia Lupparelli, University of Florence, ITALY – Stefania Galimberti, University of Milano Bicocca, ITALY

5

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Selected papers by the SFC - Société Française de Classification

TBA

Organizer: Ndeye Niang, CEDRIC CNAM, FRANCE

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Statistical learning methods in finance and business

Supervised, unsupervised and semisupervised learning methods are increasingly used in empirical investigations of financial and business problems. But there is a problem of choosing an approach, method, procedure or algorithm, which would be the most effective from an analytical and prognostic point of view and be useful for practice. The aim of the session is to give the platform for discussion and dissemination results of fruitful applications of statistical learning methods for deepening microeconomic, financial and business analyses, especially in these economically and politically unstable times.

Organizers: Józef Pociecha, Cracow University of Economics, POLAND  – Barbara Pawełek, Cracow University of Economics, POLAND

5

Barbara Pawełek et al.

Cracow University of Economics
POLAND

5

TBA

5

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