Matrix Algebra From a Statistician's Perspective / Edition 1

Matrix Algebra From a Statistician's Perspective / Edition 1

by David A. Harville
     
 

ISBN-10: 0387783563

ISBN-13: 9780387783567

Pub. Date: 06/27/2008

Publisher: Springer New York

A knowledge of matrix algebra is a prerequisite for the study of much of modern statistics, especially the areas of linear statistical models and multivariate statistics. This reference book provides the background in matrix algebra necessary to do research and understand the results in these areas. Essentially self-contained, the book is best-suited for a reader who…  See more details below

Overview

A knowledge of matrix algebra is a prerequisite for the study of much of modern statistics, especially the areas of linear statistical models and multivariate statistics. This reference book provides the background in matrix algebra necessary to do research and understand the results in these areas. Essentially self-contained, the book is best-suited for a reader who has had some previous exposure to matrices. Solultions to the exercises are available in the author's "Matrix Algebra: Exercises and Solutions."

Product Details

ISBN-13:
9780387783567
Publisher:
Springer New York
Publication date:
06/27/2008
Edition description:
1st ed. 1997. 2nd printing 2008
Pages:
634
Sales rank:
808,841
Product dimensions:
6.10(w) x 9.20(h) x 1.60(d)

Table of Contents

Preface. - Matrices. - Submatrices and partitioned matricies. - Linear dependence and independence. - Linear spaces: row and column spaces. - Trace of a (square) matrix. - Geometrical considerations. - Linear systems: consistency and compatability. - Inverse matrices. - Generalized inverses. - Indepotent matrices. - Linear systems: solutions. - Projections and projection matrices. - Determinants. - Linear, bilinear, and quadratic forms. - Matrix differentiation. - Kronecker products and the vec and vech operators. - Intersections and sums of subspaces. - Sums (and differences) of matrices. - Minimzation of a second-degree polynomial (in n variables) subject to linear constraints. - The Moore-Penrose inverse. - Eigenvalues and Eigenvectors. - Linear transformations. - References. - Index.

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