title: Nonparametric bayesian estimation of positive false discovery rates.
abstract: 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.
authors.affiliation: Department of Psychiatry, SUNY Health Science Center, Brooklyn, New York 11203, USA.
authors.forename: Yongqiang
authors.initials: Y
authors.lastname: Tang
authors.forename: Subhashis
authors.initials: S
authors.lastname: Ghosal
authors.forename: Anindya
authors.initials: A
authors.lastname: Roy
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D007938:Leukemia / Q000175:diagnosis*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*