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Article: Maximum likelihood estimation for the proportional odds model with random effects

TitleMaximum likelihood estimation for the proportional odds model with random effects
Authors
KeywordsCorrelated failure time data
Frailty model
Linear transformation model
Proportional hazards
Semiparametric efficiency
Survival data
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. 470, p. 470-483 How to Cite?
AbstractIn this article we study the semiparametric proportional odds model with random effects for correlated, right-censored failure time data. We establish that the maximum likelihood estimators for the parameters of this model are consistent and asymptotically Gaussian. Furthermore, the limiting variances achieve the semiparametric efficiency bounds and can be consistently estimated. Simulation studies show that the asymptotic approximations are accurate for practical sample sizes and that the efficiency gains of the proposed estimators over those of Cai, Cheng, and Wei can be substantial. A real example is provided to illustrate the proposed methods. © 2005 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/146562
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZeng, Den_HK
dc.contributor.authorLin, DYen_HK
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2012-05-02T08:37:01Z-
dc.date.available2012-05-02T08:37:01Z-
dc.date.issued2005en_HK
dc.identifier.citationJournal Of The American Statistical Association, 2005, v. 100 n. 470, p. 470-483en_HK
dc.identifier.issn0162-1459en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146562-
dc.description.abstractIn this article we study the semiparametric proportional odds model with random effects for correlated, right-censored failure time data. We establish that the maximum likelihood estimators for the parameters of this model are consistent and asymptotically Gaussian. Furthermore, the limiting variances achieve the semiparametric efficiency bounds and can be consistently estimated. Simulation studies show that the asymptotic approximations are accurate for practical sample sizes and that the efficiency gains of the proposed estimators over those of Cai, Cheng, and Wei can be substantial. A real example is provided to illustrate the proposed methods. © 2005 American Statistical Association.en_HK
dc.languageengen_US
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.subjectCorrelated failure time dataen_HK
dc.subjectFrailty modelen_HK
dc.subjectLinear transformation modelen_HK
dc.subjectProportional hazardsen_HK
dc.subjectSemiparametric efficiencyen_HK
dc.subjectSurvival dataen_HK
dc.titleMaximum likelihood estimation for the proportional odds model with random effectsen_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1198/016214504000001420en_HK
dc.identifier.scopuseid_2-s2.0-20444476802en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-20444476802&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume100en_HK
dc.identifier.issue470en_HK
dc.identifier.spage470en_HK
dc.identifier.epage483en_HK
dc.identifier.isiWOS:000233311300010-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZeng, D=8725807700en_HK
dc.identifier.scopusauthoridLin, DY=7403692293en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.citeulike207475-

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