Discovering the false discovery rate

By Yoav Benjamini. 2010

630 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2d8bc065924c4729e15b565eca0e7b06a8077b2c

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Automated threshold selection for extreme value analysis via ordered goodness-of-fit tests with adjustment for false discovery rate (2018, methodology): “Goodness-of-fit tests are simple to understand and perform, but error control, however, is challenging because of the ordered nature of the hypotheses, and the usual methods from multiple testing such as false discovery rate (FDR) [e.g., Benjamini (2010a, 2010b)] cannot be directly applied.”
A practical guide to methods controlling false discoveries in computational biology (2018, methodology): “The Benjamini and Hochberg step-up procedure (BH) [13, 23] was the first method proposed to control the FDR.”
Data-driven hypothesis weighting increases detection power in genome-scale multiple testing (2016, background): “V R _ 1 (1) The FDR has many advantageous features compared to existing error measures, for example it is adaptive, in the sense that the multiplicity penalty incurred adapts to the signal in the data and the number of tests [30].”
Covariate powered cross‐weighted multiple testing (2017, background): “se error rate (FWER), but for many applications this is too stringent, and over the last 20 years the false discovery rate (FDR) has become a popular choice [5], as it is more permissive and adaptive [4]. Nevertheless, practitioners often think of the multiple testing problem as a burden – a necessa”
On the interpretation of differences between groups for compositional data (2015, methodology): “There are other more sophisticated procedures, such as the Scheffe’s, the Tukey’s or the Student-NewmanKeuls tests or another different approach provided by the FDR False Discovery Rate controlling method (Benjamini and Hochberg, 1995; Benjamini, 2010).”
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). Although these works have seeded a wealth of methodological innovati”
Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection (2016, background): “Although these works have seeded a wealth of methodological innovations over the past two decades (Benjamini, 2010), all these procedures act on a set of valid p-values (or equivalent statistics), which they assume can be computed.”
Strengths and Weaknesses in the Intellectual Profile of Different Subtypes of Specific Learning Disorder (2017, methodology): “P-values were adjusted for multiple comparisons using the false discovery rate correction (Benjamini, 2010).”
Temporal Interference Stimulation Enhances Neural Regeneration (2026, unknown): “For this, the DESeq2 normalisation method was applied, the p -value calculation model followed a negative binomial distribution, and the false discovery rate (FDR) method was Benjamini–Hochberg [ 172 ].”