Colloquium - Aaditya Ramdas
Institution: Carnegie Mellon University
Title: From Universal and Sequential Inference to False Discovery Rate Control with E-values
Date: October 12, 2021
Location: Zoom (Click here for meeting details)
Time: 10:20 AM - 11:10 AM Eastern Time
This talk will gently introduce the concept of an e-value (a nonnegative random variable with expectation at most one under the null), which is an alternative to p-values, that merges frequentist, Bayesian and game-theoretic ways of thinking, and generalizes likelihood ratios and Bayes factors to nonparametric and composite settings. E-values have desirable properties for multiple testing including being automatically robust to arbitrary dependence between tests (https://arxiv.org/abs/2009.02824). To make the abstract concept of an "e-value" more concrete, I will discuss two broad settings where such e-values arise naturally, which is universal inference with the split likelihood ratio test (https://www.pnas.org/content/117/29/16880) and adaptive sequential inference in multi-armed bandits using nonnegative supermartingales (https://arxiv.org/abs/2107.07322). Extensions to estimation do exist, under the terminology "confidence sequences". In case of further interest in these topics, please check out http://stat.cmu.edu/~aramdas/sequential.html and http://stat.cmu.edu/~aramdas/multiple.html.
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