Likelihood approach for marginal proportional hazards regression in the presence of dependent censoring

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dc.creatorZeng, Donglin
dc.date2005-05-27
dc.date.accessioned2026-07-07T08:06:56Z
dc.date.available2026-07-07T08:06:56Z
dc.descriptionIn many public health problems, an important goal is to identify the effect of some treatment/intervention on the risk of failure for the whole population. A marginal proportional hazards regression model is often used to analyze such an effect. When dependent censoring is explained by many auxiliary covariates, we utilize two working models to condense high-dimensional covariates to achieve dimension reduction. Then the estimator of the treatment effect is obtained by maximizing a pseudo-likelihood function over a sieve space. Such an estimator is shown to be consistent and asymptotically normal when either of the two working models is correct; additionally, when both working models are correct, its asymptotic variance is the same as the semiparametric efficiency bound.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053604000001291 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0505604
dc.identifierhttp://arxiv.org/abs/math/0505604
dc.identifierAnnals of Statistics 2005, Vol. 33, No. 2, 501-521
dc.identifierdoi:10.1214/009053604000001291
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130775
dc.subjectStatistics Theory
dc.subject62G07 (Primary) 62F12. (Secondary)
dc.titleLikelihood approach for marginal proportional hazards regression in the presence of dependent censoring
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