Bücher der Reihe Wiley Series in Probability and Statistics

Incorporating a large body of new work in the field, this work includes the applications of modern missing data methods to real data. It also examines the theoretical and technical extensions that take advantage of computational advances.

Robust Statistics fills the need for a solid, up-to-date text that presents a broad overview of the theory of robust statistics, integrated with applications and computing. The book features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series.

* Presents a comprehensive guide to clustering techniques. * Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies * Includes a new section on how to use R for cluster analysis.

Comprehensive Reference Work on Multivariate Analysis and Its ApplicationsThe first edition of this book, by Mardia, Kent and Bibby, has been widely used globally for over 40 years. This second edition brings many topics up to date, with a special emphasis on recent developments.A wide range of material in multivariate analysis is covered, including the classical themes of multivariate normal theory, multivariate regression, inference, multidimensional scaling, factoranalysis, cluster analysis and principal component analysis. The book also now covers modern developments such as graphical models, robust estimation, statistical learning, and high-dimensional methods. The book expertly blends theory and application, providing numerous worked examples and exercises at the end of each chapter. The reader is assumed to have a basic knowledge of mathematical statistics at an undergraduate level together with an elementary understanding of linear algebra. There are appendices which provide a background in matrix algebra, a summary of univariate statistics, a collection of statistical tables and a discussion of computational aspects. The work includes coverage of:* Basic properties of random vectors, normal distribution theory, and estimation* Hypothesis testing, multivariate regression, and analysis of variance* Principal component analysis, factor analysis, and canonical correlation analysis* Cluster analysis and multidimensional scaling* New advances and techniques, including statistical learning, graphical models and regularization methods for high-dimensional dataAlthough primarily designed as a textbook for final year undergraduates and postgraduate students in mathematics and statistics, the book will also be of interest to research workers and applied scientists.