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Activity Number: 652
Type: Contributed
Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #308074
Title: Detecting Local Two-Sample Differences with Divide-Merge Optional Polya Trees: A Genetic Association Study Application
Author(s): Jacopo Soriano*+ and Li Ma
Companies: Duke University and Duke University
Keywords: Bayesian inference ; Case-control study ; Nonparametrics ; Hypothesis testing ; Genetic ; Recursive partition
Abstract:

Testing if two samples come from the same distribution and identifying local differences is challenging in multidimensional settings. We propose a new approach to address these problems by combining nonparametric probabilistic modeling and decision theory. We introduce a nonparametric process called the divide-merge optional Polya tree (dime-OPT). This process is constructed based on an optional Polya tree (OPT) process that can split into multiple OPTs on subsets of the sample space where local difference exists between the sample distributions, and can merge into a single process on parts of the space where the sample distributions are conditionally identical. Local two sample differences can be effectively detected through inference on the divide-merge state of the process on different parts of the sample space. We establish the asymptotic consistency for two-sample testing based on this process. For illustrative purposes this method is applied to a genetic association study.


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