THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY

By Yoav Benjamini, D. Yekutieli. 2001

10,837 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, methodology): “Regions identified by thresholding the p-map using the Benjamini-Yekutieli procedure at a q-value((Benjamini and Yekutieli, 2001)) of 0.”
Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures (2017, methodology): “The procedure in Benjamini and Yekutieli (2001) allows the general dependency by paying a logarithmic term loss on the FDR which makes the method very conservative.”
On the Simes inequality and its generalization (2008, methodology): “In Benjamini and Yekutieli [2], a general result”
BMC Bioinformatics (2005, positive): “The reason behind these phenomena is hard to explain because the implementation of FDR-AME is modified from the algorithms of Yekutieli et al. [12].”
Large-scale inference (2010, background): “Benjamini and Yekutieli (2005) pioneered large-scale effect size estimation, proposing the BY(q) algorithm.”
Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection (2016, methodology): “The canonical approach is to plug p-values into the Benjamini–Hochberg (BH) procedure, which controls the FDR under p-value independence and certain forms of dependence (Benjamini and Hochberg, 1995; Benjamini and Yekutieli, 2001).”
Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling (2010, methodology): “The users of GEPAS will appreciate the novelties here, which are ‘limma’ methods for one-, two- and multi-class comparison, new multiple testing correction methods (Benjamini–Hochberg (22), Benjamini–Yekutieli (23), Bonferroni, Holm (24) and Hochberg (25)) for differential gene expression.”
Ontological analysis of gene expression data: current tools, limitations, and open problems (2005, methodology): “FatiGO Percentage Step-down minP, FDR (Benjamini and Hochberg, 1995), FDR (Benjamini and Yekutieli, 2001) Flat, Tree Uploads from user 15, 49, 69, 105”
False Discovery Control for Random Fields (2004, methodology): “This gives all elements of Cn \ Cn−1 and Un = Un−1 ∪ A∈Cn\Cn−1 A and is reminiscent of the tree hypothesis testing method of Benjamini and Yekutieli (2003).”
A comparison of the Benjamini-Hochberg procedure with some Bayesian rules for multiple testing (2008, background): “Theorem 5.1 of Benjamini and Yekutieli [3] states that if the test statistics are continuous and independent then FDR of BH is equal to αm0/m.”
Operating characteristics and extensions of the false discovery rate procedure (2002, methodology): “The BH FDR procedure and its extensions in Benjamini and Yekutieli (1999) open up a whole”
Data collection and quality challenges in deep learning: a data-centric AI perspective (2021, methodology): “Unfortunately, traditional methods for controlling false discoveries like Bonferroni correction and the Benjamini-Hochberg procedure [21] assume static hypotheses where there is a fixed number of visualizations”
False discovery rate control with e‐values (2020, methodology): “To discuss the dependence structure among p-values and e-values, we rely on the notion of positive regression dependence on a subset (PRDS) of Benjamini and Yekutieli (2001), flipped when imposed on e-values.”