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

作者：Rahman A K M Fazlur^*; Lynch James D; Pena Edsel A

来源：Journal of Nonparametric Statistics, 2014, 26(3): 575-598.

DOI：10.1080/10485252.2014.906744

• Dirichlet process
• empirical Bayes
• nonparametric prior
• PL-type estimator
• sum-quota accrual

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 parametera alpha. We derive NP Band nonparametric empirical Bayes (NPEB) estimators of the survivor F) over bar = 1 - F and construct point-wise credible intervals. The resulting Bayes estimator of (F) over bar 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 alpha, 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.

• 出版日期2014