Robust Methods for Data Reduction

Alessio Farcomeni

ISBN 10: 0367783517 / ISBN 13: 9780367783518

Editore: Chapman And Hall/CRC, 2021

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nach der Bestellung gedruckt Neuware - Printed after ordering - Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis.The first part of the book illustrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data. The second part focuses on cluster and discriminant analysis. The authors explain how to perform sample reduction by finding groups in the data.Despite considerable theoretical achievements, robust methods are not often used in practice. This book fills the gap between theoretical robust techniques and the analysis of real data sets in the area of data reduction. Using real examples, the authors show how to implement the procedures in R. The code and data for the examples are available on the book's CRC Press web page.

Codice articolo 9780367783518

Dati bibliografici

• Titolo: Robust Methods for Data Reduction
• Editore: Chapman And Hall/CRC
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 0367783517
• ISBN 13: 9780367783518
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 234 L x 156 A x 16 P (millimetri) 234 millimetri di larghezza per 156 millimetri di altezza per 16 millimetri di profondità
• Peso: 456 grammi

Descrizione articolo

Riassunto

Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis.

The first part of the book illustrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data. The second part focuses on cluster and discriminant analysis. The authors explain how to perform sample reduction by finding groups in the data.

Despite considerable theoretical achievements, robust methods are not often used in practice. This book fills the gap between theoretical robust techniques and the analysis of real data sets in the area of data reduction. Using real examples, the authors show how to implement the procedures in R. The code and data for the examples are available on the book’s CRC Press web page.

Informazioni sull?autore

Alessio Farcomeni is an assistant professor in the Department of Public Health and Infectious Diseases at the University of Rome Sapienza. His work focuses on robust statistics, longitudinal models, categorical data analysis, cluster analysis, and multiple testing. He also is involved in clinical, ecological, and econometric research.

Luca Greco is an assistant professor in the Department of Law, Economics, Management and Quantitative Methods at the University of Sannio. His research interests include robust statistics, likelihood asymptotics, pseudolikelihood functions, and skew elliptical distributions.