Zhang, Jian (2008) Estimating the false discovery rate using the stochastic approximation algorithm. Biometrika, 95 (4). pp. 961-977. ISSN 0006-3444.
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.
|Uncontrolled keywords:||Ensemble averaging; False discovery rate; Microarray data analysis; Multiple hypothesis testing;Stochastic approximation.|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Jian Zhang|
|Date Deposited:||11 Oct 2012 16:57|
|Last Modified:||25 Feb 2013 14:54|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/31582 (The current URI for this page, for reference purposes)|
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