Microarrays enable to measure the expression levels of tens of thousands of genes simultaneously. One important statistical question in such experiments is which of the several thousand genes are differentially expressed. Answering this question requires methods that can deal with multiple...

A fundamental task in the analysis of data sets with many variables is screening for associations. This can be cast as a multiple testing task, where the objective is achieving high detection power while controlling type I error. We consider m hypothesis tests represented by pairs...

Microarrays enable to measure the expression levels of tens of thousands of genes simultaneously. One important statistical question in such experiments is which of the several thousand genes are differentially expressed. Answering this question requires methods that can deal with multiple...

Andrikogiannopoulou and Papakonstantinou (AP; 2019) conduct an inquiry into the bias of the False Discovery Rate (FDR) estimators of Barras, Scaillet, and Wermers (BSW; 2010). In this Reply, we replicate their results, then further explore the bias issue by (i) using different parameter values,...

It is a common practice to use resampling methods such as the bootstrap for calculating the p-value for each test when performing large scale multiple testing. The precision of the bootstrap p-values and that of the false discovery rate (FDR) relies on the number of bootstraps used for testing...

This article shows that any single-step or stepwise multiple testing procedure (asymptotically) controlling the family-wise error rate (FWER) can be augmented into procedures that (asymptotically) control tail probabilities for the number of false positives and the proportion of false positives...

We provide a method for calculating the sample size required to attain a given average power (the ratio of rejected hypotheses to the number of false hypotheses) and a given false discovery rate (the number of incorrect rejections divided by the number of rejections) in adaptive versions of the...

We present a reformulation of the Benjamini-Hochberg method that is useful in 'large-scale' multiple testing problems based on discrete test statistics and derive its basic asymptotic (as the number of hypotheses tends to infinity) properties, subsuming earlier results. A set of gene expression...

Standard tests designed to identify mutual funds with non-zero alphas are problematic, in that they do not adequately account for the presence of lucky funds. Lucky funds have significant estimated alphas, while their true alphas are equal to zero. To address this issue, this paper quantifies...