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Article: Influence diagnostics and outlier tests for semiparametric mixed models

TitleInfluence diagnostics and outlier tests for semiparametric mixed models
Authors
KeywordsCook's distance
Longitudinal data
Penalized likelihood
Repeated measure
Semiparametric regression
Smoothing spline
Issue Date2002
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB
Citation
Journal Of The Royal Statistical Society. Series B: Statistical Methodology, 2002, v. 64 n. 3, p. 565-579 How to Cite?
AbstractSemiparametric mixed models are useful in biometric and econometric applications, especially for longitudinal data. Maximum penalized likelihood estimators (MPLEs) have been shown to work well by Zhang and co-workers for both linear coefficients and nonparametric functions. This paper considers the role of influence diagnostics in the MPLE by extending the case deletion and subject deletion analysis of linear models to accommodate the inclusion of a nonparametric component. We focus on influence measures for the fixed effects and provide formulae that are analogous to those for simpler models and readily computable with the MPLE algorithm. We also establish an equivalence between the case or subject deletion model and a mean shift outlier model from which we derive tests for outliers. The influence diagnostics proposed are illustrated through a longitudinal hormone study on progesterone and a simulated example.
Persistent Identifierhttp://hdl.handle.net/10722/82952
ISSN
2015 Impact Factor: 4.222
2015 SCImago Journal Rankings: 7.429
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorFung, WKen_HK
dc.contributor.authorZhu, ZYen_HK
dc.contributor.authorWei, BCen_HK
dc.contributor.authorHe, Xen_HK
dc.date.accessioned2010-09-06T08:35:16Z-
dc.date.available2010-09-06T08:35:16Z-
dc.date.issued2002en_HK
dc.identifier.citationJournal Of The Royal Statistical Society. Series B: Statistical Methodology, 2002, v. 64 n. 3, p. 565-579en_HK
dc.identifier.issn1369-7412en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82952-
dc.description.abstractSemiparametric mixed models are useful in biometric and econometric applications, especially for longitudinal data. Maximum penalized likelihood estimators (MPLEs) have been shown to work well by Zhang and co-workers for both linear coefficients and nonparametric functions. This paper considers the role of influence diagnostics in the MPLE by extending the case deletion and subject deletion analysis of linear models to accommodate the inclusion of a nonparametric component. We focus on influence measures for the fixed effects and provide formulae that are analogous to those for simpler models and readily computable with the MPLE algorithm. We also establish an equivalence between the case or subject deletion model and a mean shift outlier model from which we derive tests for outliers. The influence diagnostics proposed are illustrated through a longitudinal hormone study on progesterone and a simulated example.en_HK
dc.languageengen_HK
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSBen_HK
dc.relation.ispartofJournal of the Royal Statistical Society. Series B: Statistical Methodologyen_HK
dc.subjectCook's distanceen_HK
dc.subjectLongitudinal dataen_HK
dc.subjectPenalized likelihooden_HK
dc.subjectRepeated measureen_HK
dc.subjectSemiparametric regressionen_HK
dc.subjectSmoothing splineen_HK
dc.titleInfluence diagnostics and outlier tests for semiparametric mixed modelsen_HK
dc.typeArticleen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/1467-9868.00351en_HK
dc.identifier.scopuseid_2-s2.0-0036427523en_HK
dc.identifier.hkuros80128en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036427523&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume64en_HK
dc.identifier.issue3en_HK
dc.identifier.spage565en_HK
dc.identifier.epage579en_HK
dc.identifier.isiWOS:000177425500014-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK
dc.identifier.scopusauthoridZhu, ZY=23487505000en_HK
dc.identifier.scopusauthoridWei, BC=7202263644en_HK
dc.identifier.scopusauthoridHe, X=7404407842en_HK

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