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Article: A general transformation class of semiparametric cure rate frailty models

TitleA general transformation class of semiparametric cure rate frailty models
Authors
KeywordsBox-Cox transformation
Cure fraction
Empirical process
Mixture cure model
NPMLE
Proportional hazards cure model
Semiparametric efficiency
Issue Date2012
Citation
Annals Of The Institute Of Statistical Mathematics, 2012, v. 64 n. 5, p. 959-989 How to Cite?
AbstractWe consider a class of cure rate frailty models for multivariate failure time data with a survival fraction. This class is formulated through a transformation on the unknown population survival function. It incorporates random effects to account for the underlying correlation, and includes the mixture cure model and the proportional hazards cure model as two special cases. We develop efficient likelihood-based estimation and inference procedures. We show that the nonparametric maximum likelihood estimators for the parameters of these models are consistent and asymptotically normal, and that the limiting variances achieve the semiparametric efficiency bounds. Simulation studies demonstrate that the proposed methods perform well in finite samples. We provide an application of the proposed methods to the data of the age at onset of alcohol dependence, from the Collaborative Study on the Genetics of Alcoholism. © 2012 The Institute of Statistical Mathematics, Tokyo.
Persistent Identifierhttp://hdl.handle.net/10722/146600
ISSN
2015 Impact Factor: 0.768
2015 SCImago Journal Rankings: 0.931
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorDiao, Gen_HK
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2012-05-02T08:37:20Z-
dc.date.available2012-05-02T08:37:20Z-
dc.date.issued2012en_HK
dc.identifier.citationAnnals Of The Institute Of Statistical Mathematics, 2012, v. 64 n. 5, p. 959-989en_HK
dc.identifier.issn0020-3157en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146600-
dc.description.abstractWe consider a class of cure rate frailty models for multivariate failure time data with a survival fraction. This class is formulated through a transformation on the unknown population survival function. It incorporates random effects to account for the underlying correlation, and includes the mixture cure model and the proportional hazards cure model as two special cases. We develop efficient likelihood-based estimation and inference procedures. We show that the nonparametric maximum likelihood estimators for the parameters of these models are consistent and asymptotically normal, and that the limiting variances achieve the semiparametric efficiency bounds. Simulation studies demonstrate that the proposed methods perform well in finite samples. We provide an application of the proposed methods to the data of the age at onset of alcohol dependence, from the Collaborative Study on the Genetics of Alcoholism. © 2012 The Institute of Statistical Mathematics, Tokyo.en_HK
dc.languageengen_US
dc.relation.ispartofAnnals of the Institute of Statistical Mathematicsen_HK
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectBox-Cox transformationen_HK
dc.subjectCure fractionen_HK
dc.subjectEmpirical processen_HK
dc.subjectMixture cure modelen_HK
dc.subjectNPMLEen_HK
dc.subjectProportional hazards cure modelen_HK
dc.subjectSemiparametric efficiencyen_HK
dc.titleA general transformation class of semiparametric cure rate frailty modelsen_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.doi10.1007/s10463-012-0354-0en_HK
dc.identifier.scopuseid_2-s2.0-84865377825en_HK
dc.identifier.hkuros223885-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84865377825&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume64en_HK
dc.identifier.issue5en_HK
dc.identifier.spage959en_HK
dc.identifier.epage989en_HK
dc.identifier.isiWOS:000307275600005-
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridDiao, G=10041280400en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.citeulike10460378-

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