Springer Statistical Learning of Complex Data (Studies in Classification, Data Analysis, and Knowledge Organization)

This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017. From the Back Cover This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017. About the Author Francesca Greselin is an Associate Professor of Statistics at the University of Milano-Bicocca, Milan, Italy. She teaches Statistics and Insurance Risks for graduate students and Inference for PhD students. Her research interests range from robust statistical methods for model-based classification and clustering, to inferential results for inequality and risk measures. She has published more than 30 scientific papers in peer-reviewed international statistics journals. Laura Deldossi is an Associate Professor of Statistics at the Università Cattolica del Sacro Cuore in Milan, Italy. Her main research interests are optimal design of experiments, Bayesian model discrimination, discrete choice models, experimental and quasi-experimental design for causal inference designs, and statistical process control. She has taught several courses:  Statistics, Applied Statistics, Data Analysis and Sample Techniques, and Design of Experiments. Luca Bagnato is an Assistant Professor of Statistics at the Università Cattolica del Sacro Cuore in Piacenza, Italy. He completed his Ph.D. in Statistics at the University of Milano-Bicocca in 2009 and received two postdoctoral fellowships: at the University of Milano-Bicocca and at the University of Verona. His research interests include time series analysis, distribution theory, mixture models, and spatial statistics. He has published more than 20 scientific papers in peer-reviewed journals. Maurizio Vichi is a Full Professor of Statistics and Chair of the Department of Statistical Sciences at Sapienza University of Rome, Italy. He is Coordinating Editor of the international journal Advances in Data Analysis and Classification, published by Springer, and actin