STA 832
Multivariate Statistical Analysis
Classical and modern statistical methods for the analysis of multivariate data. Topics include: exploratory data analysis via matrix and tensor factorizations, linear and multilinear models for vector, matrix and tensor-valued data, group invariance approaches to estimation and testing, copula models for non-Gaussian data, and high-dimensional multivariate regression and covariance estimation. Prerequisite: Statistical Science 732.
Topics
- exploratory data analysis via matrix and tensor factorizations
- linear and multilinear models for vector
- matrix and tensor-valued data
- group invariance approaches to estimation and testing
- copula models for non-Gaussian data
- and high-dimensional multivariate regression and covariance estimation
Topics explicitly listed in official catalogue description
Sources
- Course catalogue ↗2026-2027