Matrix Analysis for Statistics

James R. Schott - Collection Wiley Series in Probability and Statistics

456 pages, parution le 10/03/2005 (2^eme édition)

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Résumé

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:

Self-contained chapters, which allow readers to select individual topics or use the reference sequentially
Extensive examples and chapter-end practice exercises, many of which involve the use of matrix methods in statistical analyses
New material on elliptical distributions and new expanded coverage of such topics as eigenvalue inequalities and matrices partitioned in 2 by 2 form, in particular, results relating the rank, generalized inverse, eigenvalues of such matrices to their submatrices, and much more
Optional sections for mathematically advanced readers

L'auteur - James R. Schott

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.

Sommaire

Preface
A Review of Elementary Matrix Algebra
Vector Spaces
Eigenvalues and Eigenvectors
Matrix Factorizations and Martrix Norms
Generalized Inverses
Systems of Linear Equations
Partitioned Matrices
Special Matrices and Matrix Operations
Matrix Derivatives and Related Topics
Some Special Topics Related to Quadratic Forms
References
Index

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	James R. Schott
Collection	Wiley Series in Probability and Statistics
Parution	10/03/2005
Édition	2^eme édition
Nb. de pages	456
Format	16 x 24
Couverture	Relié
Poids	792g
Intérieur	Noir et Blanc
EAN13	9780471669838
ISBN13	978-0-471-66983-8