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Activity Number:
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287
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #310278 |
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Title:
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Nonparametric Bayesian Estimation of Positive False Discovery Rates
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Author(s):
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Anindya Roy*+ and Subhashis Ghoshal and Yongqiang Tang
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Companies:
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University of Maryland, Baltimore County and North Carolina State University and SUNY Health Science Center
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Address:
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Dept of Mathematics and Statistics, Baltimore, MD, 21250,
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Keywords:
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Dirichlet process ; Dirichlet mixture ; Markov chain Monte Carlo ; Multiple testing ; Posterior
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Abstract:
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We propose a Dirichlet process mixture model (DPMM) for the $P$-value distribution in a multiple testing problem. The DPMM allows us to obtain posterior estimates of quantities such as the proportion of true null hypothesis and the probability of rejection of a single hypothesis. We describe a Markov chain Monte Carlo (MCMC) algorithm for computing the posterior and the posterior estimates. We propose an estimator of the positive false discovery rate based on these posterior estimates and investigate the performance of the proposed estimator via simulation. We also apply our methodology to analyze a leukemia dataset.
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