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Nonparametric Bayes estimation of gap-time distribution with recurrent event data

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  • A.K.M. Fazlur Rahman
  • James D. Lynch
  • Edsel A. Peña

Abstract

Nonparametric Bayes (NPB) estimation of the gap-time survivor function governing the time to occurrence of a recurrent event in the presence of censoring is considered. In our Bayesian approach, the gap-time distribution, denoted by F , has a Dirichlet process prior with parameter α. We derive NPB and nonparametric empirical Bayes (NPEB) estimators of the survivor function F̄ =1 - F and construct point-wise credible intervals. The resulting Bayes estimator of F̄ extends that based on single-event right-censored data, and the PL-type estimator is a limiting case of this Bayes estimator. Through simulation studies, we demonstrate that the PL-type estimator has smaller biases but higher root-mean-squared errors (RMSEs) than those of the NPB and the NPEB estimators. Even in the case of a mis-specified prior measure parameter α, the NPB and the NPEB estimators have smaller RMSEs than the PL-type estimator, indicating robustness of the NPB and NPEB estimators. In addition, the NPB and NPEB estimators are smoother (in some sense) than the PL-type estimator.

Suggested Citation

  • A.K.M. Fazlur Rahman & James D. Lynch & Edsel A. Peña, 2014. "Nonparametric Bayes estimation of gap-time distribution with recurrent event data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(3), pages 575-598, September.
    • Handle: RePEc:taf:gnstxx:v:26:y:2014:i:3:p:575-598
    DOI: 10.1080/10485252.2014.906744
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    References listed on IDEAS

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    1. Debashis Ghosh & D. Y. Lin, 2000. "Nonparametric Analysis of Recurrent Events and Death," Biometrics, The International Biometric Society, vol. 56(2), pages 554-562, June.
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