Supervised and Unsupervised Statistical Data Analysis

ISBN 10: 3032030412 / ISBN 13: 9783032030412

Published by Springer Verlag GmbH, 2025

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Bibliographic Details

• Title: Supervised and Unsupervised Statistical Data Analysis
• Publisher: Springer Verlag GmbH
• Publication Date: 2025
• Language: English
• ISBN 10: 3032030412
• ISBN 13: 9783032030412
• Binding: Soft cover
• Condition: New

About this title

Synopsis

The contributions in this book offer new insights into the theoretical and practical challenges of supervised and unsupervised learning, highlighting the remarkable breadth of contemporary statistical research while maintaining methodological rigor. Innovative approaches to statistical modeling, addressing spatial dependencies and circular data structures, are presented alongside significant advances in interpretable machine learning that reconcile statistical precision with algorithmic transparency. Particularly noteworthy is the volume’s treatment of complex data structures, including novel methods for network analysis, high-dimensional clustering, temporal pattern recognition and optimization techniques. The volume interweaves methodological innovation and practical relevance, and the applications span diverse domains, including the social sciences and biomedical engineering, each demonstrating the effective translation of statistical theory into real-world impact. 

The book contains peer-reviewed contributions presented at the special edition of the 15th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, namely the International Scientific Joint Meeting of the Italian and Dutch-Flemish Classification Societies (CLADAG-VOC 2025), held in Naples, Italy, September 8–10, 2025. The conference provided fresh perspectives on the current state of research in clustering, classification and data analysis, and underpinned the value and significance of international collaboration, addressing the emerging needs of an increasingly complex data landscape and offering novel solutions to long-standing challenges in statistical data analysis.

About the Author

Antonio D'Ambrosio is a Full Professor in Statistics at the Department of Economics and Statistics of the University of Naples Federico II, Italy. His main research interests are in classification, clustering, non-parametric and computational statistics, regression modeling, and preference learning theory and modeling.

Mark de Rooij is a Full Professor of Artificial Intelligence & Data Theory at the Institute of Psychology of Leiden University, the Netherlands. His research interests are in three main areas: predictive psychometrics, regression models for categorical response variables, and longitudinal data analysis.

Kim De Roover is an Associate Professor in the Research group of Quantitative Psychology and Individual Differences, KU Leuven, Belgium. Her research interests are in factor analysis, structural equation modeling, measurement invariance, multigroup modeling, and mixture modeling.

Carmela Iorio is an Associate Professor in Statistics at the Department of Economics and Statistics, University of Naples Federico II, Italy. Her main research interests are in the development of non-parametric statistical tools for financial time series, clustering, classification, and preference rankings theory and modeling.

Michele La Rocca is a Full Professor of Statistics at the Departments of Economics and Statistics, University of Salerno, Italy. His research interests are in resampling techniques, empirical likelihood, neural networks, deep learning and extreme learning machines, robust and nonparametric inference, nonlinear time series analysis, and variable selection.