Inference of accelerated dependent competing risks model for Marshall-Olkin bivariate Weibull distribution with nonconstant parameters
Inference of accelerated dependent competing risks model for Marshall-Olkin bivariate Weibull distribution with nonconstant parameters
复制标题
参数非常数的 Marshall-Olkin 双变量 Weibull 分布的加速相关竞争风险模型的推断
DOI：
10.1016/j.cam.2019.112398
复制
发表时间：
2020-03-01
影响因子：
2.4
通讯作者：
Liu, Yiming
中科院分区：
文献类型：
--
作者：
Bai, Xuchao;Shi, Yimin;Liu, Yiming
在本文中，我们讨论了II型混合审查方案下恒定应力加速相关竞争风险模型的统计推断。依赖结构由 Marshall-Olkin 双变量 Weibull 分布建模。假设模型中的形状和尺度参数通过对数线性关系依赖于应力水平。导出模型参数的最大似然估计 (MLE)。模型参数的置信区间 (Cis) 是基于 MLE 的渐近正态性和偏差校正百分位引导法构建的。通过使用重要性抽样方法获得具有平方误差损失函数和最高后验密度 (HPD) 可信区间 (CI) 的贝叶斯估计。此外，还得出了任务时正常使用应力水平下的加速系数和可靠性的估计值。蒙特卡洛模拟研究用于评估所提出的统计推断方法的性能。使用真实的数据示例来说明本文提出的方法，并将所提出的模型与 copula 模型进行比较。 (C) 2019 Elsevier B.V. 保留所有权利。
In this paper, we discuss the statistical inference of constant-stress accelerated dependent competing risks model under Type-II hybrid censoring schemes. The dependency structure is modeled by a Marshall-Olkin bivariate Weibull distribution. Both the shape and the scale parameters in the model are assumed to be dependent on the stress levels through a log-linear relationship. The maximum likelihood estimates (MLEs) of the model parameters are derived. Confidence intervals (Cis) of the model parameters are constructed based on the asymptotic normality of MLEs and the bias-corrected percentile bootstrap method. Bayes estimates with the squared error loss function and the highest posterior density (HPD) credible intervals (CIs) are obtained by using an importance sampling method. In addition, the estimates for the accelerated coefficients and the reliability under normal use stress level at mission time are derived. A Monte Carlo simulation study is used to evaluate the performance of the proposed statistical inference methods. A real data example is used to illustrate the methodologies proposed in this paper and to compare the proposed model with the copula model. (C) 2019 Elsevier B.V. All rights reserved.