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

Autorentext

Anthony C. Atkinson is Emeritus Professor of Statistics at the London School of Economics and Political Science, UK. He is a former Joint Editor of the Journal of the Royal Statistical Society, Series B and Associate Editor of Biometrika. He is the author of more than 270 papers published in international journals of statistics and six books published by Springer, Oxford University Press and Chapman and Hall. Marco Riani is a Full Professor of Statistics and the Director of the Robust Statistics Academy (Ro.S.A.), an interdepartmental center of the University of Parma, Italy. He is currently Associate Editor of Statistical Methods and Applications, Metron and STATS. He is the author of more than 200 publications and two books published by Springer. His research interests include robust statistics, regression, multivariate analysis and classification. Aldo Corbellini is an Associate Professor of Economic Statistics at the University of Parma, Italy. His research interests are in Bayesian robust regression and estimation, outlier detection in linear and generalized linear models and robust classification of functional data. Domenico Perrotta is a Researcher at the Joint Research Centre of the European Commission in Ispra, Italy. He holds a PhD in the Computational Theory of Automatic Learning from the École Normale Supérieure de Lyon, France. His research interests span the interface between computer science and statistics, including robust methods and their application to regression and clustering problems in the domain of anti-fraud and trade data analysis. Valentin Todorov worked as a Senior Management Information Officer at the United Nations Industrial Development Organization (UNIDO) in Vienna, Austria. He received a doctoral degree in statistics from Vienna University of Technology, Austria. His main research interests are in computational aspects of robust statistics, multivariate analysis, information systems and official statistics. He has (co-)developed and maintains several R packages on CRAN.

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.