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Weighted False Discovery Rate Control in Large-Scale Multiple Testing

Author

Listed:
  • Pallavi Basu
  • T. Tony Cai
  • Kiranmoy Das
  • Wenguang Sun

Abstract

The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This article studies weighted multiple testing in a decision-theoretical framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to a genome-wide association study is discussed. Supplementary materials for this article are available online.

Suggested Citation

  • Pallavi Basu & T. Tony Cai & Kiranmoy Das & Wenguang Sun, 2018. "Weighted False Discovery Rate Control in Large-Scale Multiple Testing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1172-1183, July.
    • Handle: RePEc:taf:jnlasa:v:113:y:2018:i:523:p:1172-1183
    DOI: 10.1080/01621459.2017.1336443
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    Cited by:

    1. Pallavi Basu & Luella Fu & Alessio Saretto & Wenguang Sun, 2021. "Empirical Bayes Control of the False Discovery Exceedance," Working Papers 2115, Federal Reserve Bank of Dallas.
    2. Li Wang, 2019. "Weighted multiple testing procedure for grouped hypotheses with k-FWER control," Computational Statistics, Springer, vol. 34(2), pages 885-909, June.
    3. T. Tony Cai & Wenguang Sun & Weinan Wang, 2019. "Covariate‐assisted ranking and screening for large‐scale two‐sample inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 187-234, April.
    4. Jiaying Gu & Roger Koenker, 2020. "Invidious Comparisons: Ranking and Selection as Compound Decisions," Papers 2012.12550, arXiv.org, revised Sep 2021.

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