Sequential Selection Procedures and False Discovery Rate Control Article Swipe

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Selection (genetic algorithm) False discovery rate Multiple comparisons problem Computer science Early stopping Sequential analysis Control (management) Statistical hypothesis testing Model selection Point (geometry) Block (permutation group theory) Algorithm Mathematics Machine learning Statistics Artificial intelligence Combinatorics Geometry Chemistry Artificial neural network Gene Biochemistry

Max G’Sell , Stefan Wager , Alexandra Chouldechova , Robert Tibshirani ·

YOU? · · 2015 · Open Access · · DOI: https://doi.org/10.1111/rssb.12122 · OA: W1871418963

Summary We consider a multiple-hypothesis testing setting where the hypotheses are ordered and one is only permitted to reject an initial contiguous block H1,…,Hk of hypotheses. A rejection rule in this setting amounts to a procedure for choosing the stopping point k. This setting is inspired by the sequential nature of many model selection problems, where choosing a stopping point or a model is equivalent to rejecting all hypotheses up to that point and none thereafter. We propose two new testing procedures and prove that they control the false discovery rate in the ordered testing setting. We also show how the methods can be applied to model selection by using recent results on p-values in sequential model selection settings.