1st Edition

Robust Methods for Data Reduction

By Alessio Farcomeni, Luca Greco Copyright 2015
297 Pages
by Chapman & Hall

298 Pages 67 B/W Illustrations
by Chapman & Hall

297 Pages
by Chapman & Hall

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.... Read more

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.

Biography

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.