Nonparametric Bayesian Analysis of the Two-Sample Problem with Censoring

• Activity Number: 498
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Kan Shang* and Cavan Reilly
• Companies: University of Minnesota and University of Minnesota
• Keywords: Bayesian inference ; censored data ; Dirichlet process prior

Abstract

Testing for differences between 2 groups is a fundamental problem in statistics and due to developments in Bayesian nonparametrics 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 takes advantage of developments in Bayesian nonparametric computing. This class of tests use the connection between the Dirichlet process prior and the Wilcoxon rank sum test but extends this idea to the mixture of Dirichlet process model. Give consistency results for this class of models we develop tests 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 a RNA-Seq investigation demonstrates the practical utility of the method.