The maximum likelihood estimate in reduced-rank regression
2005 (English)In: Numerical Linear Algebra with Applications, ISSN 1070-5325, Vol. 12, no 8, 731-741 p.Article in journal (Refereed) Published
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 (c) 2005 John Wiley & Sons, Ltd.
Place, publisher, year, edition, pages
2005. Vol. 12, no 8, 731-741 p.
maximum likelihood, principal angles, reduced-rank regression, singular value decomposition
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-46069DOI: 10.1002/nla.447OAI: oai:DiVA.org:liu-46069DiVA: diva2:266965