Semiparametric transformation models and the missing information principle

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Abstract

Semiparametric transformation models play an important role in predicting survival. The linear transformation model is one class of such models which includes the proportional hazards and proportional odds models. We consider estimation of the regression coefficients in the linear transformation model, when the data are right censored and the censoring may depend on the covariate. We propose a simple estimator based on the well-known missing information principle and derive its asymptotic properties for the general case in which the censoring can depend on the covariate, but assuming that the covariate takes values in a finite set. We show how this estimator leads to another estimator recently proposed in the literature. Results of a numerical study show the correctness of the proposed inference procedures.

Original languageEnglish (US)
Pages (from-to)327-348
Number of pages22
JournalJournal of Statistical Planning and Inference
Volume115
Issue number1
DOIs
StatePublished - Jul 1 2003
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Keywords

  • Counting processes
  • Lenglart's inequality
  • Local Kaplan-Meier estimator
  • Predictable variation processes
  • Square integrable martingales
  • The Duhamel equation

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