A Bandit Approach to Multiple Testing with False Discovery Control

6 Sep 2018 Kevin Jamieson Lalit Jain

We propose an adaptive sampling approach for multiple testing which aims to maximize statistical power while ensuring anytime false discovery control. We consider $n$ distributions whose means are partitioned by whether they are below or equal to a baseline (nulls), versus above the baseline (actual positives)... (read more)

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