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Hierarchical False Discovery RateControlling Methodology

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  • Yekutieli, Daniel

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  • Yekutieli, Daniel, 2008. "Hierarchical False Discovery RateControlling Methodology," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 309-316, March.
    • Handle: RePEc:bes:jnlasa:v:103:y:2008:m:march:p:309-316
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    Cited by:

    1. Goeman Jelle J. & Finos Livio, 2012. "The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-18, January.
    2. Anders Bredahl Kock & David Preinerstorfer, 2021. "Superconsistency of Tests in High Dimensions," Papers 2106.03700, arXiv.org, revised Jan 2022.
    3. Ferreira José A. & Berkhof Johannes & Souverein Olga & Zwinderman Koos, 2009. "A Multiple Testing Approach to High-Dimensional Association Studies with an Application to the Detection of Associations between Risk Factors of Heart Disease and Genetic Polymorphisms," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-58, January.
    4. Leah H. Palm-Forster & Paul J. Ferraro & Nicholas Janusch & Christian A. Vossler & Kent D. Messer, 2019. "Behavioral and Experimental Agri-Environmental Research: Methodological Challenges, Literature Gaps, and Recommendations," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(3), pages 719-742, July.
    5. Chang, Chiu-Lan & Cai, Qingyun, 2023. "Stock return anomalies identification during the Covid-19 with the application of a grouped multiple comparison procedure," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 168-183.
    6. Steven Phillips & Yuji Takeda & Archana Singh, 2012. "Visual Feature Integration Indicated by pHase-Locked Frontal-Parietal EEG Signals," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    7. T. Tony Cai & Wenguang Sun, 2017. "Optimal screening and discovery of sparse signals with applications to multistage high throughput studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 197-223, January.
    8. Cai, Qingyun, 2018. "A scoring criterion for rejection of clustered p-values," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 180-189.
    9. Antoine Bichat & Christophe Ambroise & Mahendra Mariadassou, 2022. "Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process," Computational Statistics, Springer, vol. 37(3), pages 995-1013, July.
    10. Yoav Benjamini, 2010. "Discovering the false discovery rate," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 405-416, September.
    11. Qingyun Cai & Hock Peng Chan, 2017. "A Double Application of the Benjamini-Hochberg Procedure for Testing Batched Hypotheses," Methodology and Computing in Applied Probability, Springer, vol. 19(2), pages 429-443, June.
    12. Rina Foygel Barber & Aaditya Ramdas, 2017. "The p-filter: multilayer false discovery rate control for grouped hypotheses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1247-1268, September.
    13. Ruth Heller, 2020. "Comments on: Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 51-55, March.

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