False discovery proportion estimation by permutations: confidence for significance analysis of microarrays

Jesse Hemerik and Jelle J. Goeman

Journal of the Royal Statistical Society Series B, 2018, vol. 80, issue 1, 137-155

Abstract: Significance analysis of microarrays (SAM) is a highly popular permutation‐based multiple‐testing method that estimates the false discovery proportion (FDP): the fraction of false positive results among all rejected hypotheses. Perhaps surprisingly, until now this method had no known properties. This paper extends SAM by providing 1−α upper confidence bounds for the FDP, so that exact confidence statements can be made. As a special case, an estimate of the FDP is obtained that underestimates the FDP with probability at most 0.5. Moreover, using a closed testing procedure, this paper decreases the upper bounds and estimates in such a way that the confidence level is maintained. We base our methods on a general result on exact testing with random permutations.

Date: 2018
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