Multiple testing of one-sided hypotheses : combining Bonferroni and the bootstrap

Joseph P. Romano and Michael Wolf

In many multiple testing problems, the individual null hypotheses (i) concern univariate parameters and (ii) are one-sided. In such problems, power gains can be obtained for bootstrap multiple testing procedures in scenarios where some of the parameters are "deep in the null" by making certain adjustments to the null distribution under which to resample. In this paper, we compare a Bonferroni adjustment that is based on finite-sample considerations with certain "asymptotic" adjustments previously suggested in the literature.

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Year of publication:	June 2017
Authors:	Romano, Joseph P. ; Wolf, Michael
Publisher:	Zurich : University of Zurich, Department of Economics
Subject:	Bonferroni \| multiple hypothesis testing \| stepwise method \| Bootstrap-Verfahren \| Bootstrap approach \| Statistischer Test \| Statistical test \| Schätztheorie \| Estimation theory