Exploring Multivariate Data with the Forward Search

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The forward search provides a method of revealing the structure of data through a mixture of model fitting and informative plots. The continuous multivariate data that are the subject of this book are often analyzed as if they come from one or more normal distributions. Such analyses, including the need for transformation, may be distorted by the presence of unidentified subsets and outliers, both individual and clustered. These important features are disguised by the standard procedures of multivariate analysis. The book introduces methods that reveal the effect of each observation on fitted models and inferences.

The powerful methods of data analysis will be of importance to scientists and statisticians. Although the emphasis is on the analysis of data, theoretical developments make the book suitable for a graduate statistical course on multivariate analysis. Topics covered include principal components analysis, discriminant analysis, cluster analysis and the analysis of spatial data. S-Plus programs for the forward search are available on a web site.

This book is a companion to Atkinson and Riani's Robust Diagnostic Regression Analysis of which the reviewer for The Journal of the Royal Statistical Society wrote "I read this book, compulsive reading such as it was, in three sittings."

Anthony Atkinson is Emeritus Professor of Statistics at the London School of Economics. He is also the author of Plots, Transformations, and Regression and coauthor of Optimum Experimental Designs. Professor Atkinson has served as Editor of The Journal of the Royal Statistical Society, Series B.

Author(s): Anthony C. Atkinson, Marco Riani, Andrea Cerioli (auth.)
Series: Springer Series in Statistics
Edition: 1
Publisher: Springer-Verlag New York
Year: 2004

Language: English
Pages: 624
Tags: Statistical Theory and Methods

Front Matter....Pages i-xxiii
Examples of Multivariate Data....Pages 1-30
Multivariate Data and the Forward Search....Pages 31-88
Data from One Multivariate Distribution....Pages 89-150
Multivariate Transformations to Normality....Pages 151-228
Principal Components Analysis....Pages 229-296
Discriminant Analysis....Pages 297-366
Cluster Analysis....Pages 367-456
Spatial Linear Models....Pages 457-549
Back Matter....Pages 551-623