Large-Scale Simultaneous Hypothesis Testing

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Estimating high-dimensional intervention effects from observational data (2008, methodology): “In order to decide which causal scores should be considered “significantly high”, we use the local false discovery rate (FDR) [ 7 ].”
Control of the mean number of false discoveries, Bonferroni and stability of multiple testing (2007, methodology): “The empirical Bayes method [Efron (2003, 2004), Efron et al. (2001)], which is based on another elegant mathematical idea, serves essentially the same purpose.”
Statistical Significance and the Dichotomization of Evidence (2017, background): “An example of dichotomous thinking is provided by Gelman and Stern (2006), who show applied researchers often fail to appreciate that “the difference between ‘significant’ and ‘not significant’ is not itself statistically significant.”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “A study has suggested that coregulation of genes induces intergene correlations, which can alter the null distribution of the statistical test [160] ; local FDR approaches were introduced that empirically estimate the null distribution.”
An empirical Bayes approach to inferring large-scale gene association networks (2005, methodology): “In microarray analysis a similar approach is already successfully being used to detect differential expression ( Efron, 2003, 2004 ).”