On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics

By Yoav Benjamini, Y. Hochberg. 2000

1,873 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/42b8e76177e1501b122107999819cbee8b48f093

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In Statistics and Probability

Citations in the corpus

An empirical Bayes approach to inferring large-scale gene association networks (2005, methodology): “This parameter is either set to one, the most conservative choice as done by Benjamini and Hochberg (1995), or it may be estimated adaptively from the data (Benjamini and Hochberg, 2000; Storey, 2002).”
Identifying differentially expressed genes using false discovery rate controlling procedures (2003, methodology): “Dudoit et al. (2002) is one of the first studies to recognize the importance of the multiplicity problem as one of the key statistical issues arising in microarray data analysis.”
Large‐scale multiple testing under dependence (2009, methodology): “…that FDR is controlled at the nominal level by the Benjamini and Hochberg (1995) step-up procedure and the adaptive p-value procedure (Benjamini and Hochberg, 2000; Genovese and Wasserman, 2004) under different dependence assumptions, supporting the universal use of the conventional FDR procedures…”
Data-driven hypothesis weighting increases detection power in genome-scale multiple testing (2016, methodology): “LSL-GBH The Group BH procedure [10], where ⇡0 is estimated using the LSL estimator [29] 3 3 3 7 Clfdr In the Clfdr procedure [20], the local tdr is estimated separetely within each group and the estimates are pooled together.”
The Brief State Rumination Inventory (BSRI): Validation and Psychometric Evaluation (2018, methodology): “Given the multiple comparisons, false discovery rate was controlled with the adaptive Benjamini and Hochberg (2000) step-up procedure.”
A note on the adaptive control of false discovery rates (2004, background): “The first of these is that the fixed rejection region method (Storey, 2002) and adaptive FDR control (Benjamini and Hochberg, 2000; Benjamini et al., 2001) essentially provide the same level of control of the error rate, despite the fact that they approach the problem from opposite directions.”
Implementing false discovery rate control: increasing your power (2005, methodology): “Benjamini and Hochberg (2000) present a simple sharpening procedure for their FDR method (included in our downloadable spreadsheet program), Tests ranked by ascending p Threshold value 0.00 0.01 0.02 0.03 0.04 0.05 FDR -Benjamini and Hochberg (1995) FWER -Holm's Sequential Bonferroni FWER…”
Judicious Use of Multiple Hypothesis Tests (2005, methodology): “Several alternatives to the linear step-up procedure have subsequently been developed (Benjamini & Liu 1999; Benjamini & Hochberg 2000), including a procedure that controls the FDR under general dependency (Benjamini & Yekutieli 2001), although this procedure tends to be highly conservative.”
Some Results on the Control of the False Discovery Rate under Dependence (2007, methodology): “The BH procedure was proposed in Benjamini & Hochberg (1995) The plug-in procedure was proposed in Genovese & Wasserman (2002) and independently in Benjamini & Hochberg (2000).”
Controlling the familywise error rate with plug‐in estimator for the proportion of true null hypotheses (2009, background): “Benjamini and Hochberg (2000) discussed this issue in connection with FDR control and made use of the terms adaptive control of the FDR and adaptive multiple-test procedures whenever a plug-in estimate comes into play; see also Benjamini et al. (2005).”
Covariate‐assisted ranking and screening for large‐scale two‐sample inference (2019, background): “A celebrated finding in the FDR-literature is that incorporating the estimated proportion of non-nulls (π1 =P.θi =1/) can improve the power (Benjamini and Hochberg, 2000; Storey, 2002; Genovese and Wasserman, 2002).”
Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach (2004, methodology): “Then, for fixed λ ∈ [0, 1/, E[F̂DRλ.t/] FDR.t/.”
THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY (2001, methodology): “…speaking, the procedure actually controls the false discovery likelihood ratio, E V m 0 R m ≤ qq (30) Other procedures, which get closer to controlling the FDR at the desired level, have been offered for independent test statistics in Benjamini and Hochberg (2000), and in Benjamini and Wei (1999).”
A comparison of the Benjamini-Hochberg procedure with some Bayesian rules for multiple testing (2008, methodology): “In Benjamini and Hochberg [ 2 ] a graphical method to estimate m0 is proposed and the formula (3.3) is used to construct an adaptive version of BH.”