Matrix Analysis for Statistics 3/e [Schott] 9781119092483

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.

頁數：552
版次：第3版
年份：2017年
規格：精裝/單色
ISBN：9781119092483

1 A Review of Elementary Matrix Algebra
2 Vector Spaces
3 Eigenvalues and Eigenvectors
4 Matrix Factorizations and Matrix Norms
5 Generalized Inverses
6 Systems of Linear Equations
7 Partitioned Matrices
8 Special Matrices and Matrix Operations
9 Matrix Derivatives and Related Topics
10 Inequalities
11 Some Special Topics Related to Quadratic Forms