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A discrete modification of the Benjamini–Yekutieli procedure

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  • Döhler, Sebastian

Abstract

The Benjamini–Yekutieli procedure is a multiple testing method that controls the false discovery rate under arbitrary dependence of the p-values. A modification of this and related procedures is proposed for the case when the test statistics are discrete. It is shown that taking discreteness into account can improve upon known procedures. The performance of this new procedure is evaluated for pharmacovigilance data and in a simulation study.

Suggested Citation

  • Döhler, Sebastian, 2018. "A discrete modification of the Benjamini–Yekutieli procedure," Econometrics and Statistics, Elsevier, vol. 5(C), pages 137-147.
    • Handle: RePEc:eee:ecosta:v:5:y:2018:i:c:p:137-147
    DOI: 10.1016/j.ecosta.2016.12.002
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    References listed on IDEAS

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    1. Yoav Benjamini & Abba M. Krieger & Daniel Yekutieli, 2006. "Adaptive linear step-up procedures that control the false discovery rate," Biometrika, Biometrika Trust, vol. 93(3), pages 491-507, September.
    2. John D. Storey & Jonathan E. Taylor & David Siegmund, 2004. "Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 187-205, February.
    3. Peter B. Gilbert, 2005. "A modified false discovery rate multiple‐comparisons procedure for discrete data, applied to human immunodeficiency virus genetics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 143-158, January.
    4. Yoav Benjamini & Yosef Hochberg, 2000. "On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics," Journal of Educational and Behavioral Statistics, , vol. 25(1), pages 60-83, March.
    5. Campbell R. Harvey & Yan Liu & Heqing Zhu, 2016. "Editor's Choice … and the Cross-Section of Expected Returns," Review of Financial Studies, Society for Financial Studies, vol. 29(1), pages 5-68.
    6. Peter H. Westfall & James F. Troendle & Gene Pennello, 2010. "Multiple McNemar Tests," Biometrics, The International Biometric Society, vol. 66(4), pages 1185-1191, December.
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