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McKelvey School of Engineering

Brown School

Statistics and Data Science Seminar: Boosting e-BH via conditional calibration

Wednesday, April 17 | 11:00 AM - 12:00 PM

Sever Hall, 102

Abstract: The e-BH procedure is an e-value-based multiple testing procedure that provably controls the false discovery rate (FDR) under any dependence structure between the e-values. Despite this appealing theoretical FDR control guarantee, the e-BH procedure often suffers from low power in practice. In this paper, we propose a general framework that boosts the power of e-BH without sacrificing its FDR control under arbitrary dependence. This is achieved by the technique of conditional calibration, where we take as input the e-values and calibrate them to be a set of “boosted e-values” that are guaranteed to be no less—and are often more—powerful than the original ones. Our general framework is explicitly instantiated in three classes of multiple testing problems: (1) testing under parametric models, (2) conditional independence testing under the model-X setting, and (3) model-free conformalized selection. Extensive numerical results demonstrate that our proposed method significantly improves the power of the e-BH procedure while continuing to control the FDR.

Host: Robert Lunde

Schools

Arts & Sciences

Website

https://artsci.wustl.edu/events/stati...

Event Contact

sds@wustl.edu

Speaker Information
Speaker: Zhimei Ren, University of Pennsylvania
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