On assessing the association for bivariate current status data

Weijing Wang*, A. Adam Ding

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

Assuming that the two failure times of interest with bivariate current status data follow a bivariate copula model, we propose a two-stage estimation procedure to estimate the association parameter which is related to Kendall's tau. Asymptotic properties of the proposed semiparametric estimator show that, although the first-stage marginal estimators have a convergence rate of only n1/3, the resulting parameter estimator still converges to a normal random variable with the usual n1/2 rate. The variance of the proposed estimator can be consistently estimated. Simulation results are presented, and a community-based study of cardiovascular diseases in Taiwan provides an illustrative example.

Original languageEnglish
Pages (from-to)879-893
Number of pages15
JournalBiometrika
Volume87
Issue number4
DOIs
StatePublished - 1 Jan 2000

Keywords

  • Copula model
  • Cross-sectional data
  • Kendall's tau
  • Odds ratio
  • Pseudolikelihood
  • Semiparametric estimation

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