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More About This Textbook
Overview
A complete, self-contained introduction to matrix analysis theory and practice
Matrix methods have evolved from a tool for expressing statistical problems to an indispensable part of the development, understanding, and use of various types of complex statistical analyses. This evolution has made matrix methods a vital part of statistical education. Traditionally, matrix methods are taught in courses on everything from regression analysis to stochastic processes, thus creating a fractured view of the topic. This updated second edition of Matrix Analysis for Statistics offers readers a unique, unified view of matrix analysis theory and methods.
Matrix Analysis for Statistics, Second Edition provides in-depth, step-by-step coverage of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; the distribution of quadratic forms; and more. The subject matter is presented in a theorem/proof format, allowing for a smooth transition from one topic to another. Proofs are easy to follow, and the author carefully justifies every step. Accessible even for readers with a cursory background in statistics, yet rigorous enough for students in statistics, this new edition is the ideal introduction to matrix analysis theory and practice.
The book features:
Editorial Reviews
From the Publisher
"It is very well worth having." (Journal of the American Statistical Association, March 2009)"This book is an excellent beginning place to start learning matrix theory and properties." (Journal of Statistical Computation and Simulation, March 2006)
Booknews
An introduction to matrix analysis theory and practice, featuring self-contained chapters for flexibility in topic choice, examples and chapter exercises, and optional sections for mathematically advanced readers. Covers the most common matrix methods used in statistical applications in a theorem/proof format, and offers simple examples involving least squares regression and concepts of mean vectors and covariance matrices. For students beginning a graduate program in statistics, assuming a previous course in introductory statistics and an undergraduate course on matrices or linear algebra. Annotation c. by Book News, Inc., Portland, Or.Product Details
Related Subjects
Meet the Author
JAMES R. SCHOTT, Professor of Statistics at the University of Central Florida, received his PhD in statistics at the University of Florida. He has published extensively in the area of multivariate analysis with articles appearing in journals such as Biometrika, Journal of the American Statistical Association, and Journal of Multivariate Analysis.
Table of Contents
Preface.
1. A Review of Elementary Matrix Algebra.
2. Vector Spaces.
3. Eigenvalues and Eigenvectors.
4. Matrix Factorizations and Martrix Norms.
5. Generalized Inverses.
6. Systems of Linear Equations.
7. Partitioned Matrices.
8. Special Matrices and Matrix Operations.
9. Matrix Derivatives and Related Topics.
10. Some Special Topics Related to Quadratic Forms.
References.
Index.