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Article: ML estimation for factor analysis: EM or non-EM?

TitleML estimation for factor analysis: EM or non-EM?
Authors
KeywordsCM
ECME
EM
Factor analysis
Maximum likelihood estimation
Issue Date2008
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174
Citation
Statistics And Computing, 2008, v. 18 n. 2, p. 109-123 How to Cite?
AbstractTo obtain maximum likelihood (ML) estimation in factor analysis (FA), we propose in this paper a novel and fast conditional maximization (CM) algorithm, which has quadratic and monotone convergence, consisting of a sequence of CM log-likelihood (CML) steps. The main contribution of this algorithm is that the closed form expression for the parameter to be updated in each step can be obtained explicitly, without resorting to any numerical optimization methods. In addition, a new ECME algorithm similar to Liu's (Biometrika 81, 633-648, 1994) one is obtained as a by-product, which turns out to be very close to the simple iteration algorithm proposed by Lawley (Proc. R. Soc. Edinb. 60, 64-82, 1940) but our algorithm is guaranteed to increase log-likelihood at every iteration and hence to converge. Both algorithms inherit the simplicity and stability of EM but their convergence behaviors are much different as revealed in our extensive simulations: (1) In most situations, ECME and EM perform similarly; (2) CM outperforms EM and ECME substantially in all situations, no matter assessed by the CPU time or the number of iterations. Especially for the case close to the well known Heywood case, it accelerates EM by factors of around 100 or more. Also, CM is much more insensitive to the choice of starting values than EM and ECME. © 2007 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/82818
ISSN
2014 Impact Factor: 1.623
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZhao, JHen_HK
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorJiang, Qen_HK
dc.date.accessioned2010-09-06T08:33:45Z-
dc.date.available2010-09-06T08:33:45Z-
dc.date.issued2008en_HK
dc.identifier.citationStatistics And Computing, 2008, v. 18 n. 2, p. 109-123en_HK
dc.identifier.issn0960-3174en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82818-
dc.description.abstractTo obtain maximum likelihood (ML) estimation in factor analysis (FA), we propose in this paper a novel and fast conditional maximization (CM) algorithm, which has quadratic and monotone convergence, consisting of a sequence of CM log-likelihood (CML) steps. The main contribution of this algorithm is that the closed form expression for the parameter to be updated in each step can be obtained explicitly, without resorting to any numerical optimization methods. In addition, a new ECME algorithm similar to Liu's (Biometrika 81, 633-648, 1994) one is obtained as a by-product, which turns out to be very close to the simple iteration algorithm proposed by Lawley (Proc. R. Soc. Edinb. 60, 64-82, 1940) but our algorithm is guaranteed to increase log-likelihood at every iteration and hence to converge. Both algorithms inherit the simplicity and stability of EM but their convergence behaviors are much different as revealed in our extensive simulations: (1) In most situations, ECME and EM perform similarly; (2) CM outperforms EM and ECME substantially in all situations, no matter assessed by the CPU time or the number of iterations. Especially for the case close to the well known Heywood case, it accelerates EM by factors of around 100 or more. Also, CM is much more insensitive to the choice of starting values than EM and ECME. © 2007 Springer Science+Business Media, LLC.en_HK
dc.languageengen_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174en_HK
dc.relation.ispartofStatistics and Computingen_HK
dc.subjectCMen_HK
dc.subjectECMEen_HK
dc.subjectEMen_HK
dc.subjectFactor analysisen_HK
dc.subjectMaximum likelihood estimationen_HK
dc.titleML estimation for factor analysis: EM or non-EM?en_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0960-3174&volume=18&spage=109&epage=123&date=2008&atitle=ML+estimation+for+factor+analysis:+EM+or+non-EM?+en_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s11222-007-9042-yen_HK
dc.identifier.scopuseid_2-s2.0-41549124138en_HK
dc.identifier.hkuros145601en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-41549124138&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume18en_HK
dc.identifier.issue2en_HK
dc.identifier.spage109en_HK
dc.identifier.epage123en_HK
dc.identifier.eissn1573-1375-
dc.identifier.isiWOS:000254625500001-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZhao, JH=7410313775en_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridJiang, Q=7402523431en_HK
dc.identifier.citeulike10318479-

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