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