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Estimating the false discovery rate using the stochastic approximation algorithm

Zhang, Jian, Liang, Faming (2008) Estimating the false discovery rate using the stochastic approximation algorithm. Biometrika, 95 (4). pp. 961-977. ISSN 0006-3444. (doi:10.1093/biomet/asn036) (KAR id:31582)

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Official URL:
http://dx.doi.org/10.1093/biomet/asn036

Abstract

Testing of multiple hypotheses involves statistics that are strongly dependent in some applications,

but most work on this subject is based on the assumption of independence. We propose

a new method for estimating the false discovery rate of multiple hypothesis tests, in which the

density of test scores is estimated parametrically by minimizing the Kullback–Leibler distance

between the unknown density and its estimator using the stochastic approximation algorithm,

and the false discovery rate is estimated using the ensemble averaging method. Our method is

applicable under general dependence between test statistics. Numerical comparisons between our

method and several competitors, conducted on simulated and real data examples, show that our

method achieves more accurate control of the false discovery rate in almost all scenarios.

Item Type: Article
DOI/Identification number: 10.1093/biomet/asn036
Uncontrolled keywords: Ensemble averaging; False discovery rate; Microarray data analysis; Multiple hypothesis testing;Stochastic approximation.
Subjects: Q Science > QA Mathematics (inc Computing science)
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Funders: National Science Foundation (https://ror.org/021nxhr62)
National Cancer Institute (https://ror.org/04w2jh416)
Depositing User: Jian Zhang
Date Deposited: 11 Oct 2012 16:57 UTC
Last Modified: 12 Jul 2022 10:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/31582 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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