Basics of Matrix Algebra for Statistics with R

The R Series

by Nick Fieller

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

Genres: Programming

244 pages, Kindle Edition

First published July 1, 2015