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The maximum likelihood estimate in reduced-rank regression
Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Scientific Computing.ORCID iD: 0000-0003-2281-856X
Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Scientific Computing.ORCID iD: 0000-0002-1542-2690
2005 (English)In: Numerical Linear Algebra with Applications, ISSN 1070-5325, E-ISSN 1099-1506, Vol. 12, no 8, 731-741 p.Article in journal (Refereed) Published
Abstract [en]

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.
Keyword [en]
maximum likelihood, principal angles, reduced-rank regression, singular value decomposition
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-46069DOI: 10.1002/nla.447OAI: oai:DiVA.org:liu-46069DiVA: diva2:266965
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13

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Elden, LarsSavas, Berkant

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