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Sequential selection procedures and false discovery rate control

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  • Max Grazier G'Sell
  • Stefan Wager
  • Alexandra Chouldechova
  • Robert Tibshirani

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  • Max Grazier G'Sell & Stefan Wager & Alexandra Chouldechova & Robert Tibshirani, 2016. "Sequential selection procedures and false discovery rate control," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 423-444, March.
    • Handle: RePEc:bla:jorssb:v:78:y:2016:i:2:p:423-444
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    References listed on IDEAS

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    1. Simonsen Katy L & McIntyre Lauren M, 2004. "Using Alpha Wisely: Improving Power to Detect Multiple QTL," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-26, February.
    2. John D. Storey & Jonathan E. Taylor & David Siegmund, 2004. "Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 187-205, February.
    3. Ehud Aharoni & Saharon Rosset, 2014. "Generalized α-investing: definitions, optimality results and application to public databases," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(4), pages 771-794, September.
    4. Lin, Dongyu & Foster, Dean P. & Ungar, Lyle H., 2011. "VIF Regression: A Fast Regression Algorithm for Large Data," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 232-247.
    5. Rajen D. Shah & Richard J. Samworth, 2013. "Variable selection with error control: another look at stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 55-80, January.
    6. Wu, Yujun & Boos, Dennis D. & Stefanski, Leonard A., 2007. "Controlling Variable Selection by the Addition of Pseudovariables," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 235-243, March.
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    1. Vitor Pessoa Colombo & Jérôme Chenal & Brama Koné & Martí Bosch & Jürg Utzinger, 2022. "Using Open-Access Data to Explore Relations between Urban Landscapes and Diarrhoeal Diseases in Côte d’Ivoire," IJERPH, MDPI, vol. 19(13), pages 1-20, June.
    2. Damian Kozbur, 2020. "Analysis of Testing‐Based Forward Model Selection," Econometrica, Econometric Society, vol. 88(5), pages 2147-2173, September.
    3. Hongyu An & Boping Tian, 2025. "Unleashing the Potential of Mixed Frequency Data: Measuring Risk with Dynamic Tail Index Regression Model," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1567-1615, March.
    4. Jeng, X. Jessie & Chen, Xiongzhi, 2019. "Predictor ranking and false discovery proportion control in high-dimensional regression," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 163-175.
    5. Pengfei Wang & Wensheng Zhu, 2022. "Large‐scale covariate‐assisted two‐sample inference under dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1421-1447, December.
    6. Yu Wang & Ling Wang & Jianhua Zong & Dongxiao Lv & Shumao Wang, 2021. "Research on Loading Method of Tractor PTO Based on Dynamic Load Spectrum," Agriculture, MDPI, vol. 11(10), pages 1-14, October.
    7. Dylan Troop & Frédéric Godin & Jia Yuan Yu, 2022. "Best-Arm Identification Using Extreme Value Theory Estimates of the CVaR," JRFM, MDPI, vol. 15(4), pages 1-15, April.
    8. Shiyun Chen & Ery Arias-Castro, 2021. "On the power of some sequential multiple testing procedures," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(2), pages 311-336, April.
    9. Liang, Weijuan & Zhang, Qingzhao & Ma, Shuangge, 2024. "Hierarchical false discovery rate control for high-dimensional survival analysis with interactions," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    10. X. Jessie Jeng & Huimin Peng & Wenbin Lu, 2021. "Model Selection With Mixed Variables on the Lasso Path," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 170-184, May.
    11. Liu, Jingyuan & Sun, Ao & Ke, Yuan, 2024. "A generalized knockoff procedure for FDR control in structural change detection," Journal of Econometrics, Elsevier, vol. 239(2).
    12. James, Robert & Leung, Henry & Prokhorov, Artem, 2023. "A machine learning attack on illegal trading," Journal of Banking & Finance, Elsevier, vol. 148(C).
    13. Markus Pelger & Jiacheng Zou, 2022. "Inference for Large Panel Data with Many Covariates," Papers 2301.00292, arXiv.org, revised Mar 2023.
    14. Yang, Chiao-Yu & Lei, Lihua & Ho, Nhat & Fithian, William, 2022. "BONuS: Multiple Multivariate Testing with a Data-Adaptive Test Statistic," Research Papers 4031, Stanford University, Graduate School of Business.
    15. Weijie J Su, 2018. "When is the first spurious variable selected by sequential regression procedures?," Biometrika, Biometrika Trust, vol. 105(3), pages 517-527.
    16. Dallakyan, Aramayis & Kim, Rakheon & Pourahmadi, Mohsen, 2022. "Time series graphical lasso and sparse VAR estimation," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    17. Lu, Jiannan & Deng, Alex, 2016. "Demystifying the bias from selective inference: A revisit to Dawid’s treatment selection problem," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 8-15.
    18. Yasuyuki Matsumura & Chisato Tachibana, 2025. "Principal component analysis in econometrics: a selective inference perspective," Papers 2511.10419, arXiv.org, revised Dec 2025.
    19. Gong, Siliang & Zhang, Kai & Liu, Yufeng, 2018. "Efficient test-based variable selection for high-dimensional linear models," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 17-31.
    20. Xu Zhao & Zhongxian Zhang & Weihu Cheng & Pengyue Zhang, 2019. "A New Parameter Estimator for the Generalized Pareto Distribution under the Peaks over Threshold Framework," Mathematics, MDPI, vol. 7(5), pages 1-18, May.

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