TY  - BOOK
AU  - Gruber,Marvin H.J.
TI  - Matrix algebra for linear models
SN  - 9781118592557 (cloth)
U1  - 000SA.062 23
PY  - 2014///
CY  - New Jersey
PB  - John Wiley
KW  - Linear models (Statistics)
KW  - Matrices
N1  - Includes bibliographical references (pages 366-367) and index; 1. What Matrices are and Some Basic Operations with Them --
2. Determinants and Solving a System of Equations --
3. The Inverse of a Matrix --
4. Special Matrices and Facts about Matrices that will be Used in the Sequel --
5. Vector Spaces --
6. The Rank of a Matrix and Solutions to Systems of Equations --
7. Finding the Eigenvalues of a Matrix --
8. The Eigenvalues and Eigenvectors of Special Matrices --
9. The Singular Value Decomposition (SVD) --
10. Applications of the Singular Value Decomposition --
11. Relative Eigenvalues and Generalizations of the Singular Value Decomposition --
12. Basic Ideas about Generalized Inverses --
13. Characterizations of Generalized Inverses Using the Singular Value Decomposition --
14. Least Square and Minimum Norm Generalized Inverses --
15. More Representations of Generalized Inverses --
16. Least Square Estimators for Less than Full-Rank Models --
17. Quadratic Forms and their Probability Distributions --
18. Analysis of Variance: Regression Models and the One- and Two-Way Classification --
19. More ANOVA --
20. The General Linear Hypothesis --
21. Unconstrained Optimization Problems --
22. Constrained Minimization Problems with Linear Constraints --
23. The Gauss---Markov Theorem --
24. Ridge Regression-Type Estimators--

Answers to selected exercises--
References--
Index
N2  - A self-contained introduction to matrix analysis theory and applications in the field of statistics Comprehensive in scope, Matrix Algebra for Linear Models offers a succinct summary of matrix theory and its related applications to statistics, especially linear models.
ER  -