JSM 2005 - Toronto

Abstract #303656

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 516
Type: Contributed
Date/Time: Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #303656
Title: Estimating the False Discovery Rate Using Constrained Multinomial Likelihoods
Author(s): Irina Ostrovnaya*+ and Dan Nicolae
Companies: The University of Chicago and The University of Chicago
Address: Department of Statistics, Chicago, IL, 60637, United States
Keywords: FDR ; constrained likelihood ; dependent data
Abstract:

The False Discovery Rate (FDR) plays an important role as an error measure for simultaneous testing of large number of hypotheses. Controlling FDR involves the estimation of the proportion of true null hypotheses, and the inference requires estimating the minimum of a monotone density. We present a new estimator based on constrained multinomial likelihoods, where the constraints are given by assumptions on the shape of the distribution of the p-values. We discuss the asymptotic properties of the estimator, including consistency and asymptotic normality, and we investigate confidence intervals based on a profile likelihood. The method has the advantage of allowing extensions to dependent data. Simulations illustrate the proposed method is accurate and has adequate power.

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