Semiparametric regression analysis of multivariate doubly censored data

Shuwei Li, Tao Hu*, Tiejun TONG, Jianguo Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This article discusses regression analysis of multivariate doubly censored data with a wide class of flexible semiparametric transformation frailty models. The proposed models include many commonly used regression models as special cases such as the proportional hazards and proportional odds frailty models. For inference, we propose a nonparametric maximum likelihood estimation method and develop a new expectation–maximization algorithm for its implementation. The proposed estimators of the finite-dimensional parameters are shown to be consistent, asymptotically normal and semiparametrically efficient. We also conduct a simulation study to assess the finite sample performance of the developed estimation method, and the proposed methodology is applied to a set of real data arising from an AIDS study.

Original languageEnglish
Pages (from-to)502-526
Number of pages25
JournalStatistical Modelling
Volume20
Issue number5
DOIs
Publication statusPublished - 1 Oct 2020

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • expectation–maximization algorithm
  • frailty model
  • Maximum likelihood estimation
  • Multivariate doubly censored data
  • semiparametric efficiency

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