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.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)