Multivariate Analysis (Wiley Series in Probability and Statistics) - Hardcover

Über die Autorin bzw. den Autor

Kanti V. Mardia OBE is a Senior Research Professor in the Department of Statistics at the University of Leeds, Leverhulme Emeritus Fellow, and Visiting Professor in the Department of Statistics, University of Oxford.

John T. Kent and Charles C. Taylor are both Professors in the Department of Statistics, University of Leeds.

Von der hinteren Coverseite

Comprehensive Reference Work on Multivariate Analysis and its Applications

The first edition of this book, by Mardia, Kent and Bibby, has been 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, factor analysis, 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, copulas, normal distribution theory, and estimation
• Hypothesis testing, multivariate regression, and analysis of variance
• Principal component analysis, factor analysis, and canonical correlation analysis
• Discriminant analysis, cluster analysis, and multidimensional scaling
• New advances and techniques, including supervised and unsupervised statistical learning, graphical models and regularization methods for high-dimensional data

Although 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.