Multiple Testing of One-Sided Hypotheses: Combining Bonferroni and the Bootstrap

University of Zurich, Department of Economics, Working Paper No. 254

19 Pages Posted: 2 Aug 2017

See all articles by Joseph P. Romano

Joseph P. Romano

Stanford University - Department of Statistics

Michael Wolf

University of Zurich - Department of Economics

Date Written: June 5, 2017

Abstract

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.

Keywords: Bonferroni, Multiple Hypothesis Testing, Stepwise Method

JEL Classification: C12, C14

Suggested Citation

Romano, Joseph P. and Wolf, Michael, Multiple Testing of One-Sided Hypotheses: Combining Bonferroni and the Bootstrap (June 5, 2017). University of Zurich, Department of Economics, Working Paper No. 254, Available at SSRN: https://ssrn.com/abstract=3011365 or http://dx.doi.org/10.2139/ssrn.3011365

Joseph P. Romano (Contact Author)

Stanford University - Department of Statistics ( email )

Stanford, CA 94305
United States

Michael Wolf

University of Zurich - Department of Economics ( email )

Wilfriedstrasse 6
Zurich, 8032
Switzerland

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