A1260
Title: False discovery rate controlled robust variable selection under cellwise contamination
Authors: Xiaoya Sun - Macquarie University (Australia) [presenting]
Houying Zhu - Macquarie University (Australia)
Garth Tarr - University of Sydney (Australia)
Samuel Muller - Macquarie University (Australia)
Abstract: The increasing complexity and dimensionality of modern datasets make cellwise outliers a frequent and expected phenomenon, complicating tasks such as variable selection. While many robust variable selection methods have been developed, few effectively handle high-rate cellwise contamination, and it remains an open problem to what extent such methods can control error rates. GRALF, the Gaussian-Ranked Adaptive Lasso with False discovery rate (FDR) control, is a robust method designed for high-dimensional variable selection under cellwise contamination, taking advantage of FDR control to increase statistical reliability. GRALF builds on the framework of GR-ALasso, an effective robust variable selection method using the Gaussian-rank estimator in the Adaptive Lasso, and integrates FDR control by generating fake counterparts of the Gaussian-rank transformed variables and estimating the number of False discoveries in the optimization process. Simulation studies demonstrate GRALF's desirable performance in terms of empirical power and FDR control under various conditions. Analysis on a node-negative breast cancer dataset further validates the effectiveness of GRALF.
