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The knockoff filter of Barber and Candes (arXiv:1404.5609) is a flexible framework for multiple testing in supervised learning models, based on introducing synthetic predictor variables to control the false discovery rate (FDR).
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[author] Zhang, Cun-HuiC.-H. and Zhang, Stephanie SS. S. (2014). Confidence intervals for low dimensional parameters in high dimensional linear models. Journal of the Royal Statistical Society Series B: Statistical Methodology 76 217–242. \endbibitem
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[author] Ning, YangY. and Liu, HanH. (2017). A general theory of hypothesis tests and confidence regions for sparse high dimensional models. \endbibitem
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[author] Candès, EmmanuelE., Fan, YingyingY., Janson, LucasL. and Lv, JinchiJ. (2018). Panning for gold: ‘model-X’ knockoffs for high dimensional controlled variable selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 80 551–577. \endbibitem
2018
[author] Katsevich, EugeneE., Sabatti, ChiaraC. and Bogomolov, MarinaM. (2021). Filtering the rejection set while preserving false discovery rate control. Journal of the American Statistical Association 1–12. \endbibitem
2021
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2021
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2021
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[author] Fithian, WilliamW. and Lei, LihuaL. (2022). Conditional calibration for false discovery rate control under dependence. The Annals of Statistics 50 3091–3118. \endbibitem
2022
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2019
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2020
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[author] Shah, Rajen DR. D. and Peters, JonasJ. (2020). The hardness of conditional independence testing and the generalised covariance measure. \endbibitem
2020
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[author] Spector, AsherA. and Janson, LucasL. (2022). Powerful knockoffs via minimizing reconstructability. The Annals of Statistics 50 252–276. \endbibitem
2022
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[author] Shah, Rajen DR. D. and Bühlmann, PeterP. (2023). Double-estimation-friendly inference for high-dimensional misspecified models. Statistical Science 38 68–91. \endbibitem
2023
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[author] Barber, Rina FoygelR. F. and Candès, Emmanuel JE. J. (2015). Controlling the false discovery rate via knockoffs. The Annals of Statistics 43 2055–2085. \endbibitem
2085
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