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Activity Number: 164 - Random and Mixed Effect Models
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #323909 View Presentation
Title: LARGE-SCALE HYPOTHESIS TESTING with a THREE-COMPONENT BETA CONTAMINATION MODEL
Author(s): Ya Qi* and Richard Charnigo
Companies: and University of Kentucky
Keywords: mixture model ; beta contamination model ; microarray analysis
Abstract:

Beta mixture models are used to describe a large collection of p-values from numerous hypothesis tests, in which case one of the mixture components may be taken to be a uniform distribution (Allison et al, 2002). Dai and Charnigo (2008) referred to such a two-component mixture as a beta contamination model and provided methods for testing whether the two-component beta contamination model could be reduced to a uniform distribution. However, an empirical distribution of p-values may be more complicated than what can be described with a two-component beta contamination model. In this work, we consider a three-component beta contamination model with a parameter space which is limited to guarantee identifiability. We explore how to test a null hypothesis that a three-component model can be reduced to two components. We define a test statistic and perform simulations to identify its critical value. As a case study, we apply this test statistic to a real microarray dataset.


Authors who are presenting talks have a * after their name.

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