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dc.contributor.authorDupuy, Jean-François
dc.date.accessioned2011-06-29T10:37:51Z
dc.date.available2011-06-29T10:37:51Z
dc.date.issued2005
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6621
dc.language.isoenen
dc.subjectProportional hazards modelen
dc.subjectCovariate measurement erroren
dc.subjectNonparametric maximum likelihooden
dc.subjectAsymptotic normalityen
dc.subject.ddc519en
dc.titleThe proportional hazards model with covariate measurement erroren
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThe proportional hazards regression model is commonly used to evaluate the relationship between survival and covariates. Covariates are frequently measured with error. Substituting mismeasured values for the true covariates leads to biased estimation. Hu et al. (Biometrics 88 (1998) 447) have proposed to base estimation in the proportional hazards model with covariate measurement error on a joint likelihood for survival and the covariate variable. Nonparametric maximum likelihood estimation (NPMLE) was used and simulations were conducted to assess the asymptotic validity of this approach. In this paper, we derive a rigorous proof of asymptotic normality of the NPML estimators.en
dc.relation.isversionofjnlnameJournal of Statistical Planning and Inference
dc.relation.isversionofjnlvol135en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate2005
dc.relation.isversionofjnlpages260-275en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.jspi.2004.05.003en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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