Journal of Business and Economic Statistics [Ranked A* in Mathematical Sciences] | December 2023
https://www.tandfonline.com/doi/full/10.1080/07350015.2023.2277857
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.
Pallavi Basu is an Assistant Professor of Operations Management at the Indian School of Business (ISB), where she teaches concepts and approaches in Statistics. Her research interests include the application of statistics in finance, marketing, and other disciplines; high-dimensional statistical inference; large-scale multiple testing; and topics on causal inference.
Professor Basu is a member of the American Statistical Association, the Institute of Mathematical Statistics, and the International Indian Statistical Association. She received her PhD in Business Administration and Statistics from the USC Marshall School of Business and was a postdoctoral fellow at Tel Aviv University. She completed her undergraduate and postgraduate studies in Statistics (specialising in mathematical statistics and probability) from the Indian Statistical Institute (ISI), Kolkata.
Her current research (2023-2026) is partly funded by the Mathematical Research Impact Centric Support (MATRICS) from the Science and Engineering Research Board (SERB), Government of India.
