AdaPT: an interactive procedure for multiple testing with side information. We consider the problem of multiple-hypothesis testing with generic side information: for each hypothesis (H_i) we observe both a (p)-value (p_i) and some predictor (x_i) encoding contextual information about the hypothesis. For large-scale problems, adaptively focusing power on the more promising hypotheses (those more likely to yield discoveries) can lead to much more powerful multiple-testing procedures. We propose a general iterative framework for this problem, the adaptive (p)-value thresholding procedure which we call AdaPT, which adaptively estimates a Bayes optimal (p)-value rejection threshold and controls the false discovery rate in finite samples. At each iteration of the procedure, the analyst proposes a rejection threshold and observes partially censored (p)-values, estimates the false discovery proportion below the threshold and proposes another threshold, until the estimated false discovery proportion is below (alpha). Our procedure is adaptive in an unusually strong sense, permitting the analyst to use any statistical or machine learning method she chooses to estimate the optimal threshold, and to switch between different models at each iteration as information accrues. We demonstrate the favourable performance of AdaPT by comparing it with state of the art methods in five real applications and two simulation studies.
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References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Cao, Hongyuan; Chen, Jun; Zhang, Xianyang: Optimal false discovery rate control for large scale multiple testing with auxiliary information (2022)
- Cao, Hongyuan; Wu, Wei Biao: Testing and estimation for clustered signals (2022)
- Cui, Tingting; Wang, Pengfei; Zhu, Wensheng: Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models (2021)
- Duan, Boyan; Ramdas, Aaditya; Balakrishnan, Sivaraman; Wasserman, Larry: Interactive martingale tests for the global null (2020)
- Katsevich, Eugene; Ramdas, Aaditya: Simultaneous high-probability bounds on the false discovery proportion in structured, regression and online settings (2020)
- Durand, Guillermo: Adaptive (p)-value weighting with power optimality (2019)
- Li, Ang; Barber, Rina Foygel: Multiple testing with the structure-adaptive Benjamini-Hochberg algorithm (2019)
- Lei, Lihua; Fithian, William: AdaPT: an interactive procedure for multiple testing with side information (2018)