Analysis of competing risks data plays an important role in the lifetime data analysis. Recently Feizjavadian and Hashemi (Computational Statistics and Data Analysis, vol. 82, 19-34, 2015) provided a classical inference of a competing risks data set using four-parameter Marshall-Olkin bivariate Weibull distribution when the failure of an unit at a particular time point can happen due to more than one cause. The aim of this paper is to provide the Bayesian analysis of the same model based on a very flexible Gamma-Dirichlet prior on the scale parameters. It is observed that the Bayesian inference has certain advantages over the classical inference in this case. We provide the Bayes estimates of the unknown parameters and the associated highest posterior density credible intervals based on Gibbs sampling technique. We further consider the Bayesian inference of the model parameters assuming partially ordered Gamma-Dirichlet prior on the scale parameters when one cause is more severe than the other cause. We have extended the results for different censoring schemes also.
翻译:最近Feizjavadian和Hashen(统计与数据分析,第82卷,19-34卷,2015年)提供了一套相互竞争的风险数据集的典型推论,该数据集使用四分法Marshall-Olkin bivariate Weibull发布,因为一个单位在特定时间点因不止一个原因在某一个时间点失灵。本文的目的是根据一个非常灵活的Gamma-Drichlet在比例参数上之前的非常灵活Gamma-Drichlet对同一模型进行分析。据观察,Bayesian的推论比本案的典型推论具有某些优势。我们根据Gigbs采样技术提供了对未知参数和相关的最高远地点密度的估计。我们进一步考虑Bayesian对模型参数的推断,假设在比例参数之前部分订购的Gamma-Drichlet,如果一个原因比另一个原因更为严重的话。我们还扩大了不同审查计划的结果。