Random Matrix Theory (RMT) is a research area of statistics that seeks to understand the properties of matrices that are used in statistical theory. As standard multivariate analyses feature increasingly large datasets, RMT is helping to prove the underlying techniques that ground these analyses.

Alan Izenman’s article walks through the current day theory and applications of RMT. This research is relevant to any field that does multivariate analysis on large data sets, including physics, criminal justice, business, social sciences, and engineering.