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Article: Influence diagnostics in common principal components analysis

TitleInfluence diagnostics in common principal components analysis
Authors
KeywordsCommon principal components
Diagnostics
Influence function
Local influence
Perturbation
Restricted likelihood
Issue Date2001
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva
Citation
Journal Of Multivariate Analysis, 2001, v. 79 n. 2, p. 275-294 How to Cite?
AbstractIn principal components analysis, the influence function and local influence approaches have been well established as important diagnostic tools. In this article, we first review the generalized local influence approach in the restricted likelihood framework. We then apply the restricted likelihood local influence diagnostic in the common principal components analysis. One special part of this local influence result is an elliptical norm of the empirical influence function, which is comparable to the delection diagnostic scaled by the same matrix which requires iterative solutions for parameter estimates with every case deleted. Local influence diagnostics are constructed by some basic building blocks that are obtained directly from the maximum likelihood estimates of the parameters, and which are based on the original data and thus require less computation. A numerical example illustrates the technique and some joint influence effects are identified by the proposed method. © 2001 Academic Press.
Persistent Identifierhttp://hdl.handle.net/10722/82778
ISSN
2015 Impact Factor: 0.857
2015 SCImago Journal Rankings: 1.458
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorGu, Hen_HK
dc.contributor.authorFung, WKen_HK
dc.date.accessioned2010-09-06T08:33:19Z-
dc.date.available2010-09-06T08:33:19Z-
dc.date.issued2001en_HK
dc.identifier.citationJournal Of Multivariate Analysis, 2001, v. 79 n. 2, p. 275-294en_HK
dc.identifier.issn0047-259Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/82778-
dc.description.abstractIn principal components analysis, the influence function and local influence approaches have been well established as important diagnostic tools. In this article, we first review the generalized local influence approach in the restricted likelihood framework. We then apply the restricted likelihood local influence diagnostic in the common principal components analysis. One special part of this local influence result is an elliptical norm of the empirical influence function, which is comparable to the delection diagnostic scaled by the same matrix which requires iterative solutions for parameter estimates with every case deleted. Local influence diagnostics are constructed by some basic building blocks that are obtained directly from the maximum likelihood estimates of the parameters, and which are based on the original data and thus require less computation. A numerical example illustrates the technique and some joint influence effects are identified by the proposed method. © 2001 Academic Press.en_HK
dc.languageengen_HK
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmvaen_HK
dc.relation.ispartofJournal of Multivariate Analysisen_HK
dc.subjectCommon principal componentsen_HK
dc.subjectDiagnosticsen_HK
dc.subjectInfluence functionen_HK
dc.subjectLocal influenceen_HK
dc.subjectPerturbationen_HK
dc.subjectRestricted likelihooden_HK
dc.titleInfluence diagnostics in common principal components analysisen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0047-259X&volume=79&spage=275&epage=294&date=2001&atitle=Influence+diagnostics+in+common+principal+components+analysisen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1006/jmva.2000.1964en_HK
dc.identifier.scopuseid_2-s2.0-0035201990en_HK
dc.identifier.hkuros69475en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035201990&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume79en_HK
dc.identifier.issue2en_HK
dc.identifier.spage275en_HK
dc.identifier.epage294en_HK
dc.identifier.isiWOS:000172308500007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridGu, H=55225103300en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK

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