Matrix Algebra: Theory, Computations and Applications in Statistics
Second Edition
Springer, 2017
This book covers the theory of matrices and linear algebra for applications
in statistics. It also covers the basics of numerical analysis for computations
involving vectors and matrices.
The outline is

Preface

Table of Contents
 I. Linear Algebra
 1. Basic Vector/Matrix Structure and Notation
 2. Vectors and Vector Spaces
 3. Basic Properties of Matrices
 4. Vector/Matrix Derivatives and Integrals
 5. Matrix Factorizations and Transformations
 6. Solution of Linear Systems
 7. Evaluation of Eigenvalues and Eigenvectors
 II. Applications in Data Analysis
 8. Special Matrices and Operations Useful in Data Analysis
 9. Selected Applications in Statistics
 III. Numerical Methods and Software
 10. Numerical Methods
 11. Numerical Linear Algebra
 12. Software for Numerical Linear Algebra
 Bibliography

Subject Index
Errata.
I would appreciate
any feedback from readers  corrections, suggestions, or general
comments.
Errata for first edition.
James Gentle, jgentle@gmu.edu