Local linear regression in proportional hazards model with censored data

Xiaobing Zhao*, Xian Zhou, Xianyi Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this article we study the method of nonparametric regression based on a transformation model, under which an unknown transformation of the survival time is nonlinearly, even more, nonparametrically, related to the covariates with various error distributions, which are parametrically specified with unknown parameters. Local linear approximations and locally weighted least squares are applied to obtain estimators for the effects of covariates with censored observations. We show that the estimators are consistent and asymptotically normal. This transformation model, coupled with local linear approximation techniques, provides many alternatives to the more general proportional hazards models with nonparametric covariates.

Original languageEnglish
Pages (from-to)2761-2776
Number of pages16
JournalCommunications in Statistics - Theory and Methods
Issue number15
Publication statusPublished - Jan 2007


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