On estimation and inference in a partially linear hazard model with varying coefficients

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)931-960
Journal / PublicationAnnals of the Institute of Statistical Mathematics
Issue number5
Online published26 Oct 2013
Publication statusPublished - Oct 2014


We study estimation and inference in a marginal proportional hazards model that can handle (1) linear effects, (2) non-linear effects and (3) interactions between covariates. The model under consideration is an amalgamation of three existing marginal proportional hazards models studied in the literature. Developing an estimation and inference procedure with desirable properties for the amalgamated model is rather challenging due to the co-existence of all three effects listed above. Much of the existing literature has avoided the problem by considering narrow versions of the model. The object of this paper is to show that an estimation and inference procedure that accommodates all three effects is within reach. We present a profile partial-likelihood approach for estimating the unknowns in the amalgamated model with the resultant estimators of the unknown parameters being root- consistent and the estimated functions achieving optimal convergence rates. Asymptotic normality is also established for the estimators.

Research Area(s)

  • Failure time, Hazard function, Profile local partial-likelihood, Root-n consistency