2025/09/03 by Lin Lu, Lin Lü, Lu, Lin +8
Mathematics · Medicine · #Conformal map #Construct (python library) #False discovery rate #Model selection #Multiple comparisons problem #SARS-CoV-2 detection and testing #Selection (genetic algorithm) #State (computer science) #Statistical Methods and Inference #Statistical Methods in Clinical Trials #Statistical hypothesis testing
paper · pdf · doi:10.48550/arxiv.2509.03297
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This work studies online multiple testing with feedback, where decisions are made sequentially, and the true state of the hypothesis is revealed after decisions are made, either instantly or with a delay, and under either full or bandit feedback. We propose Generalized alpha-investing with feedback (GAIF) along with its adaptive variants, a feedback-enhanced framework that dynamically adjusts thresholds using revealed outcomes, ensuring finite-sample false discovery rate (FDR)/marginal FDR (mFDR) control. We further extend GAIF to online conformal testing by constructing valid conformal p-values and developing feedback-enhanced testing rules with finite-sample mFDR control. We also propose a feedback-driven score selection criterion to adaptively choose the candidate score that is most effective for the testing procedure, together with a theoretical analysis of its optimality. Numerical simulations and real-data applications demonstrate the effectiveness of our methods.