A general Bayesian bootstrap for censored data based on the beta-Stacy process

Andrea Arfè; Pietro Muliere

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arXiv · 2002.04081

A general Bayesian bootstrap for censored data based on the beta-Stacy process

Andrea Arfè·Pietro Muliere

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Abstract

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.

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Andrea Arfè, Pietro Muliere. 2020-02-10. A general Bayesian bootstrap for censored data based on the beta-Stacy process. https://arxiv.org/abs/2002.04081

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