Matrix Algebra: Theory, Computations and Applications in Statistics Second Edition

by James E. Gentle

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.

Third Edition

First Edition

Errata for first edition.

James Gentle, jgentle@gmu.edu