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On maximum likelihood estimation in factor analysis-An algebraic derivation
Division of Systems and Control, Department of Information Technology, Uppsala University, Uppsala, Sweden.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
2009 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 89, no 6, 1260-1262 p.Article in journal (Refereed) Published
Abstract [en]

The maximum likelihood estimate in factor analysis is typically obtained as the solution of the stationary point equation of the likelihood function. This type of derivation suffers from two problems: it is rather cumbersome and, in fact, it is incomplete as it does not include a proof that the so-obtained estimate is indeed a global maximum point of the likelihood function. In this note we present a simple algebraic derivation of the maximum likelihood estimate in factor models with spherical noise that applies to the general complex-valued data case.

Place, publisher, year, edition, pages
2009. Vol. 89, no 6, 1260-1262 p.
Keyword [en]
Factor analysis, Maximum likelihood
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-18335DOI: 10.1016/j.sigpro.2009.01.002ISI: 000264941900031Scopus ID: 2-s2.0-60749102869OAI: oai:DiVA.org:kth-18335DiVA: diva2:336381
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved

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