Permutation-based simultaneous confidence bounds for the false discovery proportion

By Jesse Hemerik, Aldo Solari, Jelle J. Goeman. Biometrika, 2018

50 citations recorded by the source corpus.

When multiple hypotheses are tested, interest is often in ensuring that the proportion of false discoveries is small with high confidence. In this paper, confidence upper bounds for the false discovery proportion are constructed, which are simultaneous over all rejection cut-offs. In particular, this allows the user to select a set of hypotheses post hoc such that the false discovery proportion lies below some constant with high confidence. Our method uses permutations to account for the dependence structure in the data. So far only Meinshausen (2006) has developed an exact, permutation-based and computationally feasible method for obtaining simultaneous false discovery proportion bounds. We propose an exact method which uniformly improves that procedure. Further, we provide a generalization of the method that lets the user select the shape of the simultaneous confidence bounds; this gives the user more freedom in determining the power properties of the method. Interestingly, several existing permutation methods, such as significance analysis of microarrays and the maxT method of Westfall & Young (1993), are obtained as special cases.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/87d03baf74d37893c626e69569153ff21e1686c4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Statistics and Probability

Citations in the corpus

Only closed testing procedures are admissible for controlling false discovery proportions (2019, methodology): “Step-down arguments are standard for FWER control and have been applied to several FDP controlling methods in the past (Blanchard et al., 2017; Goeman et al., 2019; Hemerik et al., 2019).”