Rank estimation in reduced-rank regression

Authors:
Efstathia Bura;R. Dennis Cook
Affiliations:
Department of Statistics, George Washington University, Washington, DC;School of Statistics, University of Minnesota, St. Paul, MN
Venue:
Journal of Multivariate Analysis
Year:
2003

Citing 2
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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 only requires the response vector have finite second moments. The test is extended to the nonconstant covariance case. Linear combinations of the components of the predictor vector that are estimated to be significant for modelling the responses are obtained.