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This paper is concerned with false discovery rate (FDR) control inlarge-scale multiple testing problems. We first propose a new data-driventesting procedure for controlling the FDR in large-scale t-tests for one-samplemean problem. The proposed procedure achieves exact FDR control in finitesample settings when the populations are symmetric no matter the number oftests or sample sizes. Comparing with the existing bootstrap method for FDRcontrol, the proposed procedure is computationally efficient. We show that theproposed method can control the FDR asymptotically for asymmetric populationseven when the test statistics are not independent. We further show that theproposed procedure with a simple correction is as accurate as the bootstrapmethod to the second-order degree, and could be much more effective than theexisting normal calibration. We extend the proposed procedure to two-samplemean problem. Empirical results show that the proposed procedures have betterFDR control than existing ones when the proportion of true alternativehypotheses is not too low, while maintaining reasonably good detection ability.

A New Procedure for Controlling False Discovery Rate in Large-Scale t-tests