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

Authors: Arfè, A.; Muliere, P.
Article Title: A general Bayesian bootstrap for censored data based on the beta-Stacy process
Abstract: We introduce a novel procedure to perform Bayesian non-parametric inference with right-censored data, the 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.
Keywords: ignorability; mixtures; inference; censored data; process; distributions; nonparametric approach; bayesian bootstrap; bayesian non-parametric; beta-stacy; trees
Journal Title: Journal of Statistical Planning and Inference
Volume: 222
ISSN: 0378-3758
Publisher: Elsevier B.V.  
Date Published: 2023-01-01
Start Page: 241
End Page: 251
Language: English
ACCESSION: WOS:000853254800002
DOI: 10.1016/j.jspi.2022.07.001
PROVIDER: Clarivate Analytics Web of Science
Notes: Article -- Source: Wos
Citation Impact
MSK Authors
  1. Andrea Arfe
    1 Arfe