Linear algebra, matrix theory, and calculus foundations
Shayle R. Searle, Andre I. Khuri
Comprehensive treatment of matrix algebra concepts essential for understanding statistical methods. Covers matrix operations, eigenvalues, generalized inverses, and applications to statistics. Second edition.
Sorin Mitran
Accessible introduction to linear algebra concepts with applications to data science. From University of North Carolina at Chapel Hill. Free PDF available.
Robert A. Beezer
Comprehensive first course in linear algebra from University of Puget Sound. Open textbook with clear explanations suitable for beginners. Version 3.50. Free PDF available.
Searle & Khuri's "Matrix Algebra Useful for Statistics" provides the mathematical foundation needed for understanding linear models and mixed model equations.
Matrix operations, eigenvalues and eigenvectors, generalized inverses, and matrix decompositions are fundamental to animal breeding methods.
Matrix algebra is essential for understanding Henderson's mixed model equations, relationship matrices (A, G, H), and variance component estimation.
