Schott (statistics, U. of Central Florida) offers graduate and advanced undergraduate students in statistics an introduction to matrix analysis, an essential tool for complicated statistical analysis. His topics include elementary matrix algebra, vector spaces, Eigenvalues and Eigenvectors, matrix factorizations and matrix norms, generalized inverses, systems of linear equations, partitioned matrices, special matrices and matrix operation, matrix derivatives, and quadratic forms. He includes exercises and references. Annotation ©2004 Book News, Inc., Portland, OR
An introduction to matrix analysis theory and practice, featuring self-contained chapters for flexibility in topic choice, examples and chapter exercises, and optional sections for mathematically advanced readers. Covers the most common matrix methods used in statistical applications in a theorem/proof format, and offers simple examples involving least squares regression and concepts of mean vectors and covariance matrices. For students beginning a graduate program in statistics, assuming a previous course in introductory statistics and an undergraduate course on matrices or linear algebra. Annotation c. by Book News, Inc., Portland, Or.