Nonparametric Bayesian estimation of positive false discovery rates
Nonparametric Bayesian estimation of positive false discovery rates
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DOI：
10.1111/j.1541-0420.2007.00819.x
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发表时间：
2007-12-01
期刊：
影响因子：
1.9
通讯作者：
Roy, Anindya
中科院分区：
文献类型：
--
作者：
Tang, Yongqiang;Ghosal, Subhashis;Roy, Anindya
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 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 data set.
