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A comprehensive, must-have handbook of matrix methods with a unique emphasis on statistical applications This timely book, A Matrix Handbook for Statisticians, provides a comprehensive, encyclopedic treatment of matrices as they relate to both statistical concepts and methodologies.
Gives a treatment of data oriented techniques as well as classical methods. This sourcebook emphasizes principles rather than mathematical detail, and the coverage ranges from the practical problems of graphically representing high dimensional data to the theoretical problems relating to matrices of random variables.
Requiring no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight line regression and simple analysis of variance models, this work covers the diagnostics and methods of model fitting.
Provides a survey of aspects of model building and statistical inference. This book presents a synthesis of theoretical literature, requiring only familiarity with linear regression methods. It contains three chapters on central computational questions that comprise a self contained introduction to unconstrained optimization.
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