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Screening for Partial Conjunction Hypotheses

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  • Yoav Benjamini
  • Ruth Heller

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  • Yoav Benjamini & Ruth Heller, 2008. "Screening for Partial Conjunction Hypotheses," Biometrics, The International Biometric Society, vol. 64(4), pages 1215-1222, December.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:4:p:1215-1222
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00984.x
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    References listed on IDEAS

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    1. 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.
    2. Yoav Benjamini & Daniel Yekutieli, 2005. "False Discovery Rate-Adjusted Multiple Confidence Intervals for Selected Parameters," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 71-81, March.
    3. Loughin, Thomas M., 2004. "A systematic comparison of methods for combining p-values from independent tests," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 467-485, October.
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    Citations

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    Cited by:

    1. David Amar & Ron Shamir & Daniel Yekutieli, 2017. "Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-22, August.
    2. 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.
    3. Qunhua Li & Feipeng Zhang, 2018. "A regression framework for assessing covariate effects on the reproducibility of high‐throughput experiments," Biometrics, The International Biometric Society, vol. 74(3), pages 803-813, September.
    4. Philippe Vindras & Michel Desmurget & Pierre Baraduc, 2012. "When One Size Does Not Fit All: A Simple Statistical Method to Deal with Across-Individual Variations of Effects," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-15, June.
    5. Eric F. Lock & David B. Dunson, 2017. "Bayesian genome- and epigenome-wide association studies with gene level dependence," Biometrics, The International Biometric Society, vol. 73(3), pages 1018-1028, September.
    6. Saumard, Adrien & Wellner, Jon A., 2018. "Efron’s monotonicity property for measures on R2," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 212-224.
    7. Hillary Koch & Cheryl A. Keller & Guanjue Xiang & Belinda Giardine & Feipeng Zhang & Yicheng Wang & Ross C. Hardison & Qunhua Li, 2022. "CLIMB: High-dimensional association detection in large scale genomic data," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. 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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