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Communications in Statistics - Simulation and Computation Volume 50, 2021 - Issue 11
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Original Articles

Correcting false discovery rates for their bias toward false positives

David R. BickelOttawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, CanadaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-9623-3946View further author information
ORCID Icon &
Abbas RahalOttawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, CanadaView further author information
Pages 3699-3713 | Received 20 Jul 2018, Accepted 05 Jun 2019, Published online: 19 Jun 2019

Citations (7)

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Read on this site (4)

David R. Bickel. (2023) Fiducialize statistical significance: transforming p-values into conservative posterior probabilities and Bayes factors. Statistics 57:4, pages 941-959.
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David R. Bickel. (2021) Null Hypothesis Significance Testing Defended and Calibrated by Bayesian Model Checking. The American Statistician 75:3, pages 249-255.
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David R. Bickel. (2021) Null Hypothesis Significance Testing Interpreted and Calibrated by Estimating Probabilities of Sign Errors: A Bayes-Frequentist Continuum. The American Statistician 75:1, pages 104-112.
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David R. Bickel & Abbas Rahal. (2019) Model fusion and multiple testing in the likelihood paradigm: shrinkage and evidence supporting a point null hypothesis. Statistics 53:6, pages 1187-1209.
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Articles from other publishers (3)

Xiaoya Sun & Yan Fu. (2023) Local false discovery rate estimation with competition‐based procedures for variable selection. Statistics in Medicine.
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David R. Bickel. (2023) The p-value interpreted as the posterior probability of explaining the data: Applications to multiple testing and to restricted parameter spaces. Sankhya A.
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David R. Bickel. (2020) Testing prediction algorithms as null hypotheses: Application to assessing the performance of deep neural networks. Stat 9:1.
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