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Rank estimation in reduced-rank regression

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Author Info
Bura, Efstathia
Cook, R. Dennis
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.

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File URL: http://www.sciencedirect.com/science/article/B6WK9-48KFC1H-2/2/fa9fbde55af0754c6377dcd8fc46a3ec
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Article provided by Elsevier in its journal Journal of Multivariate Analysis.

Volume (Year): 87 (2003)
Issue (Month): 1 (October)
Pages: 159-176
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Handle: RePEc:eee:jmvana:v:87:y:2003:i:1:p:159-176

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Keywords: Asymptotic test Chi-squared Weighted chi-squared;

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  1. Stephen G. Donald & Natércia Fortuna & Vladas Pipiras, 2005. "On rank estimation in symmetric matrices: the case of indefinite matrix estimators," FEP Working Papers 167, Universidade do Porto, Faculdade de Economia do Porto. [Downloadable!]
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