Title: Rank Estimation in Reduced-Rank Regression

Speaker: Efstathia Bura
	 The George Washington University

Abstract:

Reduced rank regression assumes that the coefficient matrix in a 
multivariate regression model is not of full rank. The unknown rank 
is traditionally estimated under the assumption of normal responses. 
We derive an asymptotic test for the rank that requires no distributional 
assumptions on the response vector. The test is extended to the non-constant
 covariance case. Linear combinations of the components of the predictor 
vector that are estimated to be significant for modelling the responses 
are obtained.