Student Seminar Series
DATE: Tuesday, January 15th, 1:00pm
LOCATION: MSB 1147 (Colloquium Room).
SPEAKERS: Haoran Li, PhD Student, Statistics, UC Davis
TITLE: “High-Dimensional General Linear Hypothesis Tests via Spectral Shrinkage”
ABSTRACT: In statistics, one of the fundamental inferential problems is to test a general linear hypothesis of regression coefficients under a linear model. The framework includes many well-studied problems such as two-sample tests for equality of population means, MANOVA and others as special cases. The testing problem is well-studied in the classical multivariate analysis literature, but remains underexplored under high dimensional settings. Various classical invariant tests, despite their popularity in multivariate analysis, involve the inverse of the residual covariance matrix, which is inconsistent or even singular when the dimension is at least comparable to the degree of freedom. Consequently, classical tests perform poorly and power enhanced procedures are in need.