Multivariate Analysis

Wed, 11 Nov 2026 13:49:25 +1100

This page pairs well with Statistics, Probability and Linear Algebra. It condenses my notes for UNSW MATH5855 (Multivariate Analysis).

Multivariate analysis studies \(p\) variables measured on the same \(n\) units at once. Almost every classical method in it is one of two things: a likelihood-ratio test under the multivariate normal, or an eigenproblem for a symmetric matrix built from \(\mathbf{S}\). So the page opens with the matrix facts that make the eigenproblems tractable, builds the normal and its sampling theory, and then runs through the methods: inference on means, correlation structure, dimension reduction (PCA, CCA, factor analysis), linear models (MANOVA, structural equations), and finally classification, clustering and copulas, where normality is relaxed or dropped.

Multivariate-Normal on Aayush Bajaj's Augmenting Infrastructure

Multivariate Analysis