An adaptive step-down procedure with proven FDR control under independence

Abstract

In this work we study an adaptive step-down procedure for testing $m$ hypotheses. It stems from the repeated use of the false discovery rate controlling the linear step-up procedure (sometimes called BH), and makes use of the critical constants $iq/[(m+1-i(1-q)]$, $i=1,...,m$. Motivated by its success as a model selection procedure, as well as by its asymptotic optimality, we are interested in its false discovery rate (FDR) controlling properties for a finite number of hypotheses. We prove this step-down procedure controls the FDR at level $q$ for independent test statistics. We then numerically compare it with two other procedures with proven FDR control under independence, both in terms of power under independence and FDR control under positive dependence.

Published in at http://dx.doi.org/10.1214/07-AOS586 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (1)

• On the false discovery rate and an asymptotically optimal rejection curve

Cited by in corpus (6)

• On the false discovery rate and an asymptotically optimal rejection curve
• Two simple sufficient conditions for FDR control
• Optimal weighting for false discovery rate control
• A simple forward selection procedure based on false discovery rate control
• Adaptive FDR control under independence and dependence
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