A Scan Procedure for Multiple Testing
Statistics Theory
2018-08-03 v1 Statistics Theory
Abstract
In a multiple testing framework, we propose a method that identifies the interval with the highest estimated false discovery rate of P-values and rejects the corresponding null hypotheses. Unlike the Benjamini-Hochberg method, which does the same but over intervals with an endpoint at the origin, the new procedure `scans' all intervals. In parallel with \citep*{storey2004strong}, we show that this scan procedure provides strong control of asymptotic false discovery rate. In addition, we investigate its asymptotic false non-discovery rate, deriving conditions under which it outperforms the Benjamini-Hochberg procedure. For example, the scan procedure is superior in power-law location models.
Cite
@article{arxiv.1808.00631,
title = {A Scan Procedure for Multiple Testing},
author = {Shiyun Chen and Andrew Ying and Ery Arias-Castro},
journal= {arXiv preprint arXiv:1808.00631},
year = {2018}
}