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Bayesian inference of hidden gamma wear process model for survival data with ties
Statistica Sinica ( IF 1.4 ) Pub Date : 2015-01-01 , DOI: 10.5705/ss.2012.351
Arijit Sinha 1 , Zhiyi Chi 2 , Ming-Hui Chen 2
Affiliation  

Survival data often contain tied event times. Inference without careful treatment of the ties can lead to biased estimates. This paper develops the Bayesian analysis of a stochastic wear process model to fit survival data that might have a large number of ties. Under a general wear process model, we derive the likelihood of parameters. When the wear process is a Gamma process, the likelihood has a semi-closed form that allows posterior sampling to be carried out for the parameters, hence achieving model selection using Bayesian deviance information criterion. An innovative simulation algorithm via direct forward sampling and Gibbs sampling is developed to sample event times that may have ties in the presence of arbitrary covariates; this provides a tool to assess the precision of inference. An extensive simulation study is reported and a data set is used to further illustrate the proposed methodology.

中文翻译:

带有关系的生存数据隐藏伽马磨损过程模型的贝叶斯推理

生存数据通常包含绑定的事件时间。没有仔细处理关系的推断可能会导致有偏差的估计。本文开发了随机磨损过程模型的贝叶斯分析,以拟合可能具有大量联系的生存数据。在一般磨损过程模型下,我们推导出参数的似然性。当磨损过程为 Gamma 过程时,似然具有半封闭形式,允许对参数进行后验采样,从而使用贝叶斯偏差信息准则实现模型选择。开发了一种通过直接前向采样和吉布斯采样的创新模拟算法,以对在存在任意协变量的情况下可能具有联系的事件时间进行采样;这提供了一种评估推理精度的工具。
更新日期:2015-01-01
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