论文标题
基于Beta-Stacy过程的一般贝叶斯引导程序，用于审查数据
A general Bayesian bootstrap for censored data based on the beta-Stacy process
论文作者
论文摘要
我们介绍了一个新的程序，以使用右审核数据（\ emph {beta-stacy bootstrap}执行贝叶斯非参数推断。这近似于生存分布的摘要的后定律（例如，平均生存时间）。更确切地说，我们的程序近似于β-静态过程的功能的联合后定律，这是一个非参数过程，在此之前概括了dirichlet过程，并且广泛用于生存分析。 Beta-Stacy Bootstrap概括并统一其他常见的贝叶斯引导程序，以基于非参数先验的完整或审查数据。它是由不需要调整马尔可夫链蒙特卡洛步骤的精确采样算法来定义的。我们通过分析来自真实临床试验的生存数据来说明Beta-Stacy Bootstrap。
We introduce a novel procedure to perform Bayesian non-parametric inference with right-censored data, the \emph{beta-Stacy bootstrap}. This approximates the posterior law of summaries of the survival distribution (e.g. the mean survival time). More precisely, our procedure approximates the joint posterior law of functionals of the beta-Stacy process, a non-parametric process prior that generalizes the Dirichlet process and that is widely used in survival analysis. The beta-Stacy bootstrap generalizes and unifies other common Bayesian bootstraps for complete or censored data based on non-parametric priors. It is defined by an exact sampling algorithm that does not require tuning of Markov Chain Monte Carlo steps. We illustrate the beta-Stacy bootstrap by analyzing survival data from a real clinical trial.
