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| 文件名: Basics of Matrix Algebra for Statistics with R.rar | |
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Basics of Matrix Algebra for Statistics with R
 Features Covers basic algebraic manipulation of matrices, such as basic arithmetic, inversion, partitioning, rank, determinants, decompositions, eigenanalysis, and Hadamard and Kronecker products Shows how to implement the techniques in R using worked numerical examples Describes vector and matrix calculus, including differentiation of scalars and linear and quadratic forms Incorporates useful tricks, such as identifying rank 1 matrices and scalar subfactors within products Explains how to convert an optimization problem to an eigenanalysis by imposing a non-restrictive constraint Presents the derivation of key results in linear models and multivariate methods with step-by-step cross-referenced explanations Includes numerous theoretical and numerical exercises for self-assessment |
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