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Matrix Algebra Useful for Statistics

Publisher: 
John Wiley
Number of Pages: 
438
Price: 
85.00
ISBN: 
978-0-470-00961-1

This book is a survey of matrix algebra commonly used in multivariate statistics. It is an uncorrected reprint of an edition of the book that was first published in 1982. The book begins with basic topics in linear algebra, including operations on matrices, determinants, solving linear equations, inverses, rank, eigenvalues, and canonical forms. This is followed by chapters on applications of linear algebra in statistics, particularly in regression analysis. Although this book is still useful, the material is somewhat out of date. Harville (1997) is a more up-to-date reference.

References

Harville, David A., Matrix Algebra From a Statistician's Perspective . Springer, 1997.


Brian Borchers is a professor of Mathematics at the New Mexico Institute of Mining and Technology. His interests are in optimization and applications of optimization in parameter estimation and inverse problems.
Date Received: 
Tuesday, February 13, 2007
Reviewable: 
Yes
Include In BLL Rating: 
No
Shayle R. Searle
Series: 
Wiley Series in Probability and Statistics
Publication Date: 
2006
Format: 
Paperback
Category: 
Textbook
Brian Borchers
03/25/2007

1. Introduction.

2. Basic Operations.

3. Special Matrices.

4. Determinants.

5. Inverse Matrices.

6. Rank.

7. Canonical Forms.

8. Generalized Inverses.

9. Solving Linear Equations.

10. Partitioned Matrices.

11. Eigenvalues and Eigenvectors.

11A. Appendix to Chapter 11.

12. Miscellanea.

13. Applications in Statistics.

14. The Matrix Algebra of Regression Analysis.

15. An Introduction to Linear Statistical Models.

References.

Index.

Publish Book: 
Modify Date: 
Sunday, March 25, 2007

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