Empirical likelihood confidence regions for one- or two- samples with doubly censored data

Junshan Shen, Kam Chuen Yuen, Chunling Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The purpose is to propose a new EM algorithm for doubly censored data subject to nonparametric moment constraints. Empirical likelihood confidence regions are constructed for one- or two- samples of doubly censored data. It is shown that the corresponding empirical likelihood ratio converges to a standard chi-square random variable. Simulations are carried out to assess the finite-sample performance of the proposed method. For illustration purpose, the proposed method is applied to a real data set with two samples.
Original languageEnglish
Pages (from-to)285-293
Number of pages9
JournalComputational Statistics and Data Analysis
Volume93
DOIs
Publication statusPublished - 1 Jan 2016

Keywords

  • Chi-square convergence
  • Confidence region
  • Doubly censored data
  • EM algorithm
  • Empirical likelihood ratio
  • Moment constraint

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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