Alessio Farcomeni, Luca Greco

Robust Methods for Data Reduction (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book 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. 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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 297
• Erscheinungstermin: 13. Januar 2016
• Englisch
• ISBN-13: 9781466590632
• Artikelnr.: 43824296

Autorenporträt

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.

Inhaltsangabe

Introduction and Overview. Multivariate Estimation Methods. Dimension
Reduction: Principal Component Analysis. Sparse Robust PCA. Canonical
Correlation Analysis. Factor Analysis. Sample Reduction: k-Means and
Model-Based Clustering. Robust Clustering. Robust Model-Based Clustering.
Double Clustering. Discriminant Analysis. Appendix. Bibliography. Index.