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Article: Robust estimation in generalized partial linear models for clustered data

TitleRobust estimation in generalized partial linear models for clustered data
Authors
KeywordsB-spline
Estimating equation
Generalized linear model
Longitudinal data
Robustness
Issue Date2005
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main
Citation
Journal Of The American Statistical Association, 2005, v. 100 n. 472, p. 1176-1184 How to Cite?
AbstractIn this article we consider robust generalized estimating equations for the analysis of semiparametric generalized partial linear models (GPLMs) for longitudinal data or clustered data in general. We approximate the nonparametric function in the GPLM by a regression spline, and use bounded scores and leverage-based weights in the estimating equation to achieve robustness against outliers. We show that the regression spline approach avoids some of the intricacies associated with the profile-kernel method, and that robust estimation and inference can be carried out operationally as if a generalized linear model were used. © 2005 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/82703
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHe, Xen_HK
dc.contributor.authorFung, WKen_HK
dc.contributor.authorZhu, Zen_HK
dc.date.accessioned2010-09-06T08:32:26Z-
dc.date.available2010-09-06T08:32:26Z-
dc.date.issued2005en_HK
dc.identifier.citationJournal Of The American Statistical Association, 2005, v. 100 n. 472, p. 1176-1184en_HK
dc.identifier.issn0162-1459en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82703-
dc.description.abstractIn this article we consider robust generalized estimating equations for the analysis of semiparametric generalized partial linear models (GPLMs) for longitudinal data or clustered data in general. We approximate the nonparametric function in the GPLM by a regression spline, and use bounded scores and leverage-based weights in the estimating equation to achieve robustness against outliers. We show that the regression spline approach avoids some of the intricacies associated with the profile-kernel method, and that robust estimation and inference can be carried out operationally as if a generalized linear model were used. © 2005 American Statistical Association.en_HK
dc.languageengen_HK
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=mainen_HK
dc.relation.ispartofJournal of the American Statistical Associationen_HK
dc.subjectB-splineen_HK
dc.subjectEstimating equationen_HK
dc.subjectGeneralized linear modelen_HK
dc.subjectLongitudinal dataen_HK
dc.subjectRobustnessen_HK
dc.titleRobust estimation in generalized partial linear models for clustered dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0162-1459&volume=100&issue=472&spage=1176&epage=1184&date=2005&atitle=Robust+estimation+in+generalized+partial+linear+models+for+clustered+dataen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1198/016214505000000277en_HK
dc.identifier.scopuseid_2-s2.0-29144468067en_HK
dc.identifier.hkuros120348en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-29144468067&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume100en_HK
dc.identifier.issue472en_HK
dc.identifier.spage1176en_HK
dc.identifier.epage1184en_HK
dc.identifier.isiWOS:000233581100012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridHe, X=7404407842en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK
dc.identifier.scopusauthoridZhu, Z=23487505000en_HK
dc.identifier.citeulike383421-

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