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Estimating the false discovery rate using the stochastic approximation algorithm

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  • Faming Liang
  • Jian Zhang

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. Copyright 2008, Oxford University Press.

Suggested Citation

  • Faming Liang & Jian Zhang, 2008. "Estimating the false discovery rate using the stochastic approximation algorithm," Biometrika, Biometrika Trust, vol. 95(4), pages 961-977.
    • Handle: RePEc:oup:biomet:v:95:y:2008:i:4:p:961-977
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    File URL: http://hdl.handle.net/10.1093/biomet/asn036
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    Cited by:

    1. Sun, Yan & Song, Qifan & Liang, Faming, 2022. "Learning sparse deep neural networks with a spike-and-slab prior," Statistics & Probability Letters, Elsevier, vol. 180(C).
    2. Faming Liang & Momiao Xiong, 2013. "Bayesian Detection of Causal Rare Variants under Posterior Consistency," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-16, July.
    3. Pan, Lanfeng & Li, Yehua & He, Kevin & Li, Yanming & Li, Yi, 2020. "Generalized linear mixed models with Gaussian mixture random effects: Inference and application," Journal of Multivariate Analysis, Elsevier, vol. 175(C).

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