**Date/Time**

Date(s) - 18/11/2022*3:30 pm - 4:30 pm*

__Speaker: Patrick Flaherty (UMass-Amherst)__

Title: Cost-aware Generalized alpha-investing for Multiple Hypothesis Testing

Abstract: We consider the problem of sequential multiple hypothesis testing with nontrivial data collection cost. This problem appears, for example, when conducting biological experiments to identify differentially expressed genes in a disease process. This work builds on the generalized alpha-investing framework that enables control of the false discovery rate in a sequential testing setting. We make a theoretical analysis of the long term asymptotic behavior of alpha-wealth which motivates a consideration of sample size in the alpha-investing decision rule. Using the game theoretic principle of indifference, we construct a decision rule that optimizes the expected return (ERO) of alpha-wealth and provides an optimal sample size for the test. We show empirical results that a cost-aware ERO decision rule correctly rejects more false null hypotheses than other methods. We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to hedge against the risk of unproductive tests. Finally, empirical tests on a real data set from a biological experiment show that cost-aware ERO produces actionable decisions as to which tests to conduct and if so at what sample size.

Preprint: https://arxiv.org/abs/2210.17514

Location: Hamilton Hall 305

Coffee and Cookies will be served in Hamilton Hall 305 at 3:00 pm. Everyone is welcome.