This book is concerned with data in which the observations are independent and in which the response is multivariate.

Companion book to Robust Diagnostic Regression Analysis (ISBN 0-387-95017-6) published by Springer in 2000 Includes supplementary material: sn.pub/extras

Klappentext

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.

Inhalt
1 Examples of Multivariate Data.- 2 Multivariate Data and the Forward Search.- 3 Data from One Multivariate Distribution.- 4 Multivariate Transformations to Normality.- 5 Principal Components Analysis.- 6 Discriminant Analysis.- 7 Cluster Analysis.- 8 Spatial Linear Models.- Appendix: Tables of Data.- Author Index.