Title: Empirical Bayes control of the false discovery exceedance
Authors: Pallavi Basu - Indian School of Business (India) [presenting]
Abstract: An empirical Bayes procedure is proposed that guarantees control of the False Discovery eXceedance (FDX) by ranking and thresholding hypotheses based on their local false discovery rate (lfdr) test statistic. In a two-group independent model or Gaussian with exchangeable hypotheses, we show that ranking by the lfdr delivers the ``optimal'' ranking for FDX control. We propose a computationally efficient procedure that does not empirically lose validity and power and illustrate its properties by analyzing two million stock trading strategies.