Reduced-rank regression for the multivariate linear model
The problem of estimating the regression coefficient matrix having known (reduced) rank for the multivariate linear model when both sets of variates are jointly stochastic is discussed. We show that this problem is related to the problem of deciding how many principal components or pairs of canonical variates to use in any practical situation. Under the assumption of joint normality of the two sets of variates, we give the asymptotic (large-sample) distributions of the various estimated reduced-rank regression coefficient matrices that are of interest. Approximate confidence bounds on the elements of these matrices are then suggested using either the appropriate asymptotic expressions or the jackknife technique.
Year of publication: |
1975
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Authors: | Izenman, Alan Julian |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 5.1975, 2, p. 248-264
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Publisher: |
Elsevier |
Keywords: | Multivariate linear regression principal components canonical variates asymptotic distribution theory confidence bounds jackknife |
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