Multivariate Analysis - (Wiley Probability and Statistics) 2nd Edition by Kanti V Mardia & John T Kent & Charles C Taylor (Hardcover)
About this item
Highlights
- Multivariate Analysis 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.
- About the Author: 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.
- 592 Pages
- Mathematics, Probability & Statistics
- Series Name: Wiley Probability and Statistics
Description
About the Book
"For over 40 years the first edition of this book (which was also translated into Persian) has been used by students to acquire a basic knowledge of the theory and methods of multivariate statistical analysis. The book has also served the wider statistical community to further their understanding of this field. Plans for the second edition started almost 20 years ago, and we have struggled with questions about which topics to add- something of a moving target in a field which has continued to evolve in this new era of "big data". Since the first edition was published, multivariate analysis has been developed and extended in many directions. This new edition aims to bring the first edition up to date by substantial revision, rewriting and additions, whilst seeking to maintain the overall length of the book. The basic approach has been maintained, namely, a mathematical treatment of statistical methods for observations consisting of several measurements or characteristics of each subject and a study of their properties. The core topics, and the structure many of the chapters, have been retained"--Book Synopsis
Multivariate AnalysisComprehensive 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.
From the Back Cover
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.
About the Author
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.
