Semiparametric Analysis of Truncated Data

Jing Qin, Mei Cheng Wang

Research output: Contribution to journalArticle

Abstract

Randomly truncated data are frequently encountered in many studies where truncation arises as a result of the sampling design. In the literature, nonparametric and semiparametric methods have been proposed to estimate parameters in one-sample models. This paper considers a semiparametric model and develops an efficient method for the estimation of unknown parameters. The model assumes that K populations have a common probability distribution but the populations are observed subject to different truncation mechanisms. Semiparametric likelihood estimation is studied and the corresponding inferences are derived for both parametric and nonparametric components in the model. The method can also be applied to two-sample problems to test the difference of lifetime distributions. Simulation results and a real data analysis are presented to illustrate the methods.

Original languageEnglish (US)
Pages (from-to)225-242
Number of pages18
JournalLifetime Data Analysis
Volume7
Issue number3
DOIs
StatePublished - Dec 1 2001

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Keywords

  • AIDS
  • Biased sampling problems
  • Bootstrap resampling
  • Truncated data

ASJC Scopus subject areas

  • Applied Mathematics

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