An Empirical Bayes Approach to Controlling the False Discovery Exceedance
提出一种经验贝叶斯方法控制错误发现超限（FDX），通过排序和阈值化局部错误发现率（lfdr）的最优决策规则，并用计算捷径模拟该规则，在模拟和真实数据中验证效果，应用于识别异常股票交易策略。
In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the false discovery proportion (FDP) is highly variable. We develop an empirical Bayes approach to control 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 the FDX constraint. We propose a data-driven FDX procedure that uses carefully designed computational shortcuts to emulate the oracle rule. We investigate the empirical performance of the proposed method using both simulated and real data and study the merits of FDX control through an application for identifying abnormal stock trading strategies.