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Balanced Control of Generalized Error Rates


Joseph P. Romano


Stanford University - Department of Statistics

Michael Wolf


Department of Economics; Department of Economics

July 2008

University of Zurich Institute for Empirical Research in Economics Working Paper No. 379

Abstract:     
Consider the problem of testing s hypotheses simultaneously. In this paper, we derive methods which control the generalized familywise error rate given by the probability of k or more false rejections, abbreviated k-FWER. We derive both single-step and stepdown procedures that control the k-FWER in finite samples or asymptotically, depending on the situation. Moreover, the procedures are asymptotically balanced in an appropriate sense. We briefly consider control of the average number of false rejections. Additionally, we consider the false discovery proportion (FDP), defined as the number of false rejections divided by the total number of rejections (and defined to be 0 if there are no rejections). Here, the goal is to construct methods which satisfy, for given s and a, P{FDP > s} <= a , at least asymptotically. Special attention is paid to the construction of methods which implicitly take into account the dependence structure of the individual test statistics in order to further increase the ability to detect false null hypotheses. A general resampling and subsampling approach is presented which achieves these objectives, at least asymptotically.

Number of Pages in PDF File: 40

Keywords: Bootstrap, False Discovery Proportion, Generalized familywise error rate, Multiple Testing, Stepdown procedure

JEL Classification: C12, C14

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Date posted: August 26, 2008  

Suggested Citation

Romano, Joseph P. and Wolf, Michael, Balanced Control of Generalized Error Rates (July 2008). University of Zurich Institute for Empirical Research in Economics Working Paper No. 379 . Available at SSRN: http://ssrn.com/abstract=1258775 or http://dx.doi.org/10.2139/ssrn.1258775

Contact Information

Joseph P. Romano (Contact Author)
Stanford University - Department of Statistics ( email )
Stanford, CA 94305
United States
Michael Wolf
Department of Economics ( email ) ( email )
Rämistrasse 71
Zurich, 8006
Switzerland
Department of Economics ( email ) ( email )
Rämistrasse 71
Zurich, 8006
Switzerland
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