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.

翻译：我们引入了一种新程序,用右检查数据,即 \ emph{ beta- Stacy 靴杆来进行巴伊西亚非参数的推断。 这近似于生存分布摘要(例如平均存活时间)的后遗法。 更准确地说, 我们的程序近似于贝塔- Stacy 工艺功能的后遗法, 这是一种非参数性的过程, 之前将 Dirichlet 过程概括化, 并在生存分析中广泛使用。 贝塔西靴套套将其他常见的巴伊西亚靴套子概括化, 并统一其他基于非参数前遗迹的完整或审查数据。 它由精确的抽样算法定义, 不需要对 Markov 链 蒙特卡洛 步骤进行调整。 我们通过分析真正的临床试验中的生存数据来说明乙蒂- Stacy 靴套。