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The maximum likelihood estimate in reduced-rank regression

✍ Scribed by Lars Eldén; Berkant Savas

Publisher
John Wiley and Sons
Year
2005
Tongue
English
Weight
109 KB
Volume
12
Category
Article
ISSN
1070-5325

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✦ Synopsis

Abstract

In previous work by Stoica and Viberg the reduced‐rank regression problem is solved in a maximum likelihood sense. The present paper proposes an alternative numerical procedure. The solution is written in terms of the principal angles between subspaces spanned by the data matrices. It is demonstrated that the solution is meaningful also in the case when the maximum likelihood criterion is not valid. A numerical example is given. Copyright © 2005 John Wiley & Sons, Ltd.

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