A general Bayesian bootstrap for censored data based on the betaStacy process
Abstract
We introduce a novel procedure to perform Bayesian nonparametric inference with rightcensored data, the \emph{betaStacy 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 betaStacy process, a nonparametric process prior that generalizes the Dirichlet process and that is widely used in survival analysis. The betaStacy bootstrap generalizes and unifies other common Bayesian bootstraps for complete or censored data based on nonparametric priors. It is defined by an exact sampling algorithm that does not require tuning of Markov Chain Monte Carlo steps. We illustrate the betaStacy bootstrap by analyzing survival data from a real clinical trial.
 Publication:

arXiv eprints
 Pub Date:
 February 2020
 DOI:
 10.48550/arXiv.2002.04081
 arXiv:
 arXiv:2002.04081
 Bibcode:
 2020arXiv200204081A
 Keywords:

 Statistics  Methodology;
 Mathematics  Statistics Theory;
 Statistics  Computation