EFFICIENT ESTIMATION FOR DIMENSION REDUCTION WITH CENSORED SURVIVAL DATA

Ge Zhao, Yanyuan Ma, Wenbin Lu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We propose a general index model for survival data, that generalizes many commonly used semiparametric survival models and belongs to the framework of dimension reduction. Using a combination of a geometric approach in semiparametrics and a martingale treatment in survival data analysis, we devise estimation procedures that are feasible and do not require covariate-independent censoring, as assumed in many dimension-reduction methods for censored survival data. We establish the root-n consistency and asymptotic normality of the proposed estimators and derive the most efficient estimator in this class for the general index model. Numerical experiments demonstrate the empirical performance of the proposed estimators, and an application to an AIDS data set further illustrates the usefulness of the work.

Original languageEnglish (US)
Pages (from-to)2359-2380
Number of pages22
JournalStatistica Sinica
Volume32
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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