Empirical Bayes Control of the False Discovery Exceedance

Pallavi Basu (), Luella Fu (), Alessio Saretto and Wenguang Sun ()
Additional contact information
Pallavi Basu: https://www.isb.edu/en/research-thought-leadership/faculty/faculty-directory/pallavi-basu.html
Wenguang Sun: https://www.marshall.usc.edu/personnel/wenguang-sun

No 2115, Working Papers from Federal Reserve Bank of Dallas

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 (search for similar items in EconPapers)
JEL-codes: C11 C12 C15 (search for similar items in EconPapers)
Pages: 33
Date: 2021-11-18
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ore
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DOI: 10.24149/wp2115

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