Non parametric Bayesian analysis of the two-sample problem with censoring

Kan Shang, Cavan Reilly

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Testing for differences between two groups is a fundamental problem in statistics, and due to developments in Bayesian non parametrics and semiparametrics there has been renewed interest in approaches to this problem. Here we describe a new approach to developing such tests and introduce a class of such tests that take advantage of developments in Bayesian non parametric computing. This class of tests uses the connection between the Dirichlet process (DP) prior and the Wilcoxon rank sum test but extends this idea to the DP mixture prior. Here tests are developed that have appropriate frequentist sampling procedures for large samples but have the potential to outperform the usual frequentist tests. Extensions to interval and right censoring are considered and an application to a high-dimensional data set obtained from an RNA-Seq investigation demonstrates the practical utility of the method.

Original languageEnglish (US)
Pages (from-to)12008-12022
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Volume46
Issue number24
DOIs
StatePublished - Dec 17 2017

Keywords

  • Bayesian inference
  • Dirichlet process prior
  • censored data

Fingerprint Dive into the research topics of 'Non parametric Bayesian analysis of the two-sample problem with censoring'. Together they form a unique fingerprint.

Cite this