Date: 2023-08-17
Time: 15:30-16:30 (Montreal time)
Hybrid: In person / Zoom
Location: Burnside Hall 1104
https://mcgill.zoom.us/j/89623344755?pwd=S1E0QWVjSm8wRHdIYU5IZzllSXNjUT09
Meeting ID: 896 2334 4755
Passcode: 287381
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.
Speaker
Pallavi Basu is an Assistant Professor in the Operations Management area at Indian School of Business (ISB). Her research interests include-Applications of statistics in finance, marketing, and other disciplines; High-dimensional statistical inference and Large-scale multiple testing. She is a member of the American Statistical Association, Institute of Mathematical Statistics and International Indian Statistical Association. She has gained her Ph.D. (Business Administration and Statistics), from USC Marshall School of Business.