Inferences on the association parameter in copula models for bivariate survival data

J. H. Shih, T. A. Louis

Research output: Contribution to journalArticle

Abstract

We investigate two-stage parametric and two-stage semi-parametric estimation procedures for the association parameter in copula models for bivariate survival data where censoring in either or both components is allowed. We derive asymptotic properties of the estimators and compare their performance by simulations. Both parametric and semi-parametric estimators of the association parameter are efficient at independence, and the parameter estimates in the margins have high efficiency and are robust to misspecification of dependency structures. In addition, we propose a consistent variance estimator for the semi-parametric estimator of the association parameter. We apply the proposed methods to an AIDS data set for illustration.

Original languageEnglish (US)
Pages (from-to)1384-1399
Number of pages16
JournalBiometrics
Volume51
Issue number4
DOIs
StatePublished - Dec 1 1995
Externally publishedYes

Keywords

  • Association
  • Bivariate failure times
  • Copula models
  • Semi-parametric models
  • Time-dependent correlation coefficient

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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