Estimating the false discovery rate using the stochastic approximation algorithm

Zhang, Jian and Liang, Faming (2008) Estimating the false discovery rate using the stochastic approximation algorithm. Biometrika, 95 (4). pp. 961-977. ISSN 0006-3444. (doi:https://doi.org/10.1093/biomet/asn036) (Full text available)

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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
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:	Faculties > Sciences > School of Mathematics Statistics and Actuarial Science Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User:	Jian Zhang
Date Deposited:	11 Oct 2012 16:57 UTC
Last Modified:	14 Jul 2014 08:05 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/31582 (The current URI for this page, for reference purposes)