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
Basics of Matrix Algebra for Statistics with R.rar
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