Working Paper

Empirical Bayes Control of the False Discovery Exceedance

Abstract: In sparse large-scale testing problems where the false discovery proportion (FDP) is highly variable, the false discovery exceedance (FDX) provides a valuable alternative to the widely used false discovery rate (FDR). We develop an empirical Bayes approach to controlling the FDX. We show that for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to FDX constraint. We propose a data-driven FDX procedure that emulates the oracle via carefully designed computational shortcuts. We investigate the empirical performance of the proposed method using simulations and illustrate the merits of FDX control through an application for identifying abnormal stock trading strategies.

Keywords: Cautious Data Mining; False Discovery Exceedance Control; Local False Discovery Rates; Multiple Hypotheses Testing; Poisson Binomial Distribution; Trading Strategies;

JEL Classification: C11; C12; C15;

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Bibliographic Information

Provider: Federal Reserve Bank of Dallas

Part of Series: Working Papers

Publication Date: 2021-11-18

Number: 2115