
Matrix Analysis for Statistics
James R. Schott(Author)
Wiley-Blackwell (Publisher)
2nd Edition
Published on 11. February 2005
Book
Hardback
480 pages
978-0-471-66983-8 (ISBN)
Article exhausted; check for reprint
Description
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.
Reviews / Votes
"This book is an excellent beginning place to start learning matrix theory and properties." (Journal of Statistical Computation and Simulation, March 2006)More details
Series
Edition
2. Auflage
Language
English
Place of publication
Chicester
United Kingdom
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
Edition type
Revised edition
Dimensions
Height: 24.1 cm
Width: 17.2 cm
Thickness: 3.2 cm
Weight
888 gr
ISBN-13
978-0-471-66983-8 (9780471669838)
Schweitzer Classification
Other editions
New editions

James R. Schott
Matrix Analysis for Statistics
Book
08/2016
3rd Edition
Wiley
€129.00
Shipment within 15-20 days
Previous edition
James R. Schott
Matrix Analysis for Statistics
Book
10/1996
Wiley
€73.67
Article exhausted; check for reprint
Person
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.
Content
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.