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.
