Robust Methods for Data Reduction

Robust Methods for Data Reduction provides a non-technical overview of robust data reduction techniques, encouraging their use in practical applications. It covers principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis, with examples in R and code and data available on the CRC Press web page.

Format: Paperback / softback
Length: 297 pages
Publication date: 31 March 2021
Publisher: Taylor & Francis Ltd

Robust Methods for Data Reduction provides a comprehensive and non-technical introduction to robust data reduction techniques, emphasizing their practical applications. The book covers various important methods, including principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis. The first part of the book demonstrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data, while the second part focuses on cluster and discriminant analysis. The authors explain how to perform sample reduction by finding groups in the data and provide real-world examples of implementing the procedures in R. The code and data for the examples are available on the book's CRC Press web page, making it a valuable resource for practitioners and researchers in the field of data reduction. Despite the significant theoretical advancements in robust methods, they are not widely used in practice. This book aims to bridge the gap between theoretical robust techniques and the analysis of real data sets in the area of data reduction, providing practical guidance and examples for implementing the procedures in R.

Weight: 549g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367783518