Power of Ordered Hypothesis Testing

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Lihua Lei, William Fithian ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2924-2932, 2016.

Abstract

Ordered testing procedures are multiple testing procedures that exploit a pre-specified ordering of the null hypotheses, from most to least promising. We analyze and compare the power of several recent proposals using the asymptotic framework of Li & Barber (2015). While accumulation tests including ForwardStop can be quite powerful when the ordering is very informative, they are asymptotically powerless when the ordering is weaker. By contrast, Selective SeqStep, proposed by Barber & Candes (2015), is much less sensitive to the quality of the ordering. We compare the power of these procedures in different regimes, concluding that Selective SeqStep dominates accumulation tests if either the ordering is weak or non-null hypotheses are sparse or weak. Motivated by our asymptotic analysis, we derive an improved version of Selective SeqStep which we call Adaptive SeqStep, analogous to Storey’s improvement on the Benjamini-Hochberg procedure. We compare these methods using the GEO-Query data set analyzed by (Li & Barber, 2015) and find Adaptive SeqStep has favorable performance for both good and bad prior orderings.

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