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Studies in Classification, Data Analysis, and Knowledge Organization

Statistical Learning of Complex Data

Editors: Greselin, F., Deldossi, L., Bagnato, L., Vichi, M. (Eds.)

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  • Presents the latest findings in classification, statistical learning, (big) data analysis and related areas
  • Highlights a variety of applications in economics, architecture, medicine, data management, consumer behavior and the gender gap
  • Focuses on methodological and computational aspects, as well as real-world problems
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eBook $109.00
price for USA in USD (gross)
  • Due: November 27, 2019
  • ISBN 978-3-030-21140-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
Softcover $149.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week).
  • Due: October 30, 2019
  • ISBN 978-3-030-21139-4
  • Free shipping for individuals worldwide
About this book

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 authors

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 acting Chair of the European Statistical Advisory Committee of the EU. He teaches Multivariate Statistics and Advances in Data Analysis and Statistical Modelling. His research interests include statistical models for clustering, classification, dimensionality reduction, composite indicators, PLS, SEM and new methods for official statistics based on smart statistics and big data analysis. He is the author of more than 150 papers, mainly published in peer-reviewed international statistics journals.

Table of contents (20 chapters)

Table of contents (20 chapters)
  • Cluster Weighted Beta Regression: A Simulation Study

    Pages 3-11

    Alfó, Marco (et al.)

  • Detecting Wine Adulterations Employing Robust Mixture of Factor Analyzers

    Pages 13-21

    Cappozzo, Andrea (et al.)

  • Simultaneous Supervised and Unsupervised Classification Modeling for Assessing Cluster Analysis and Improving Results Interpretability

    Pages 23-31

    Fordellone, Mario (et al.)

  • A Parametric Version of Probabilistic Distance Clustering

    Pages 33-43

    Rainey, Christopher (et al.)

  • An Overview on the URV Model-Based Approach to Cluster Mixed-Type Data

    Pages 45-53

    Ranalli, Monia (et al.)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • Due: November 27, 2019
  • ISBN 978-3-030-21140-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
Softcover $149.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week).
  • Due: October 30, 2019
  • ISBN 978-3-030-21139-4
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Statistical Learning of Complex Data
Editors
  • Francesca Greselin
  • Laura Deldossi
  • Luca Bagnato
  • Maurizio Vichi
Series Title
Studies in Classification, Data Analysis, and Knowledge Organization
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-21140-0
DOI
10.1007/978-3-030-21140-0
Softcover ISBN
978-3-030-21139-4
Series ISSN
1431-8814
Edition Number
1
Number of Pages
XIV, 201
Number of Illustrations
27 b/w illustrations, 9 illustrations in colour
Topics