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Abstract:
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It is a common practice to use resampling methods such as bootstrap for calculating p-value for each test when performing large scale multiple testing. However, the required number of bootstraps can be computationally burdensome. Further adding to the computational challenge is that the calculation of the test statistic itself may require considerable computation time. As technology improves one can expect the dimension of the problem to increase as well. Motivated by this important need, we developed a simple adaptive bootstrap methodology for large scale multiple testing, which reduces the total number of bootstrap calculations while ensuring the control of the FDR. Based on simulation study we found that, relative to the number of bootstraps required for the Benjamini-Hochberg procedure, the proposed methodology achieved a very substantial reduction in the number of bootstraps.
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