Matrix Analysis for Statistics
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BeschreibungMatrix Analysis for Statistics, Second Edition provides in-depth, step-by-step coverage of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors, the Moore-Penrose inverse, matrix differentiation, the distribution of quadratic forms, and more. The subject matter is presented in a theorem/proof format, allowing for a smooth transition from one topic to another. Proofs are easy to follow, and the author carefully justifies every step. Accessible even for readers with a cursory background in statistics, yet rigorous enough for students in statistics, this new edition is the ideal introduction to matrix analysis theory and practice.
1. A Review of Elementary Matrix Algebra.
2. Vector Spaces.
3. Eigenvalues and Eigenvectors.
4. Matrix Factorizations and Martrix 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. Some Special Topics Related to Quadratic Forms.
PortraitJAMES R. SCHOTT, Professor of Statistics at the University of Central Florida, received his PhD in statistics at the University of Florida. He has published extensively in the area of multivariate analysis with articles appearing in journals such as Biometrika, Journal of the American Statistical Association, and Journal of Multivariate Analysis.
Pressestimmen"This book is an excellent beginning place to start learning matrix theory and properties." (Journal of Statistical Computation and Simulation, March 2006)
Untertitel: Revised. Sprache: Englisch.
Verlag: JOHN WILEY & SONS INC
Erscheinungsdatum: Januar 2005
Seitenanzahl: 480 Seiten