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Bayesian estimation and stochastic model specification search for dynamic survival models

机译:动态生存模型的贝叶斯估计和随机模型规范搜索

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摘要

Dynamic survival models are a useful extension of the popular Cox model as the effects of explanatory variables are allowed to change over time. In this paper a new auxiliary mixture sampler for Bayesian estimation of the model parameters is introduced. This sampler forms the basis of a model space MCMC method for stochastic model specification search in dynamic survival models, which involves selection of covariates to include in the model as well as specification of effects as time-varying or constant. The method is applied to two well-known data sets from the literature.
机译:动态生存模型是流行的Cox模型的有用扩展,因为允许解释变量的影响随时间变化。本文介绍了一种用于模型参数贝叶斯估计的新型辅助混合采样器。该采样器构成了用于动态生存模型中的随机模型规格搜索的模型空间MCMC方法的基础,该方法涉及选择要包括在模型中的协变量以及时变或常数效应规格。该方法被应用于文献中的两个众所周知的数据集。

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