Recently, Feizjavadian and Hashemi (Analysis of dependent competing risks in presence of progressive hybrid censoring using Marshall–Olkin bivariate Weibull distribution. Comput Stat Data Anal. 2015;82:19–34) provided a classical inference of a competing risks data set using 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 (GD) prior on the scale parameters. 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 GD 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.

最近，Feizjavadian 和 Hashemi（使用 Marshall-Olkin 双变量 Weibull 分布分析存在渐进式混合审查的相关竞争风险。计算统计数据分析。2015；82:19-34）提供了使用 Marshall 的竞争风险数据集的经典推断– 当一个单元在特定时间点的故障可能由于多个原因而发生时，Olkin 双变量 Weibull 分布。本文的目的是基于非常灵活的 Gamma-Dirichlet (GD) 先验尺度参数，对同一模型进行贝叶斯分析。在这种情况下，贝叶斯推理比经典推理具有一定的优势。我们提供了基于 Gibbs 采样技术的未知参数的贝叶斯估计和相关的最高后验密度可信区间。当一个原因比另一个原因更严重时，我们进一步考虑模型参数的贝叶斯推断，假设部分有序 GD 先于尺度参数。我们还扩展了不同审查方案的结果。