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stat.ME2026
Virtual Dummies: Enabling Scalable FDR-Controlled Variable Selection via Sequential Sampling of Null Features
Taulant Koka, Jasin Machkour, Daniel P. Palomar +1
High-dimensional variable selection, particularly in genomics, requires error-controlling procedures that scale to millions of predictors. The Terminating-Random Experiments (T-Rex…
stat.ME2026
Learning False Discovery Rate Control via Model-Based Neural Networks
Arnau Vilella, Jasin Machkour, Michael Muma +1
Controlling the false discovery rate (FDR) in high-dimensional variable selection requires balancing rigorous error control with statistical power. Existing methods with provable g…