**James R. Schott ...**552 pages -

**Publisher:**Wiley; 3rd edition (June, 2016) ...

**Language:**English -

**ISBN-10:**1119092485 -

**ISBN-13:**978-1119092483 ...

**An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice:**Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications,

*Matrix Analysis for Statistics, Third Edition*features an easy-to-follow theorem/proof format. Featuring smooth transitions between topical coverage, the author carefully justifies the step-by-step process of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; and the distribution of quadratic forms. An ideal introduction to matrix analysis theory and practice,

*Matrix Analysis for Statistics, Third Edition*features: • New chapter or section coverage on inequalities, oblique projections, and antieigenvalues and antieigenvectors • Additional problems and chapter-end practice exercises at the end of each chapter • Extensive examples that are familiar and easy to understand • Self-contained chapters for flexibility in topic choice • Applications of matrix methods in least squares regression and the analyses of mean vectors and covariance matrices.

*Matrix Analysis for Statistics, Third Edition*is an ideal textbook for upper-undergraduate and graduate-level courses on matrix methods, multivariate analysis, and linear models. The book is also an excellent reference for research professionals in applied statistics.