title: A robust method for large-scale multiple hypotheses testing.
abstract: When drawing large-scale simultaneous inference, such as in genomics and imaging problems, multiplicity adjustments should be made, since, otherwise, one would be faced with an inflated type I error. Numerous methods are available to estimate the proportion of true null hypotheses pi(0), among a large number of hypotheses tested. Many methods implicitly assume that the pi(0) is large, that is, close to 1. However, in practice, mid-range pi(0) values are frequently encountered and many of the widely used methods tend to produce highly variable or biased estimates of pi(0). As a remedy in such situations, we propose a hierarchical Bayesian model that produces an estimator of pi(0) that exhibits considerably less bias and is more stable. Simulation studies seem indicative of good method performance even when low-to-moderate correlation exists among test statistics. Method performance is assessed in simulated settings and its practical usefulness is illustrated in an application to a type II diabetes study.
authors.affiliation: Department of Statistics, University of Wisconsin-Madison, Medical Science Center 1300 University Avenue, Madison, WI 53706, USA.
authors.forename: Seungbong
authors.initials: S
authors.lastname: Han
authors.forename: Adin-Cristian
authors.initials: AC
authors.lastname: Andrei
authors.forename: Kam-Wah
authors.initials: KW
authors.lastname: Tsui
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008954:Models, Biological; D015233:Models, Statistical
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