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Variable selection with error control: another look at stability selection

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  • Rajen D. Shah
  • Richard J. Samworth

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  • 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.
    • Handle: RePEc:bla:jorssb:v:75:y:2013:i:1:p:55-80
    DOI: 10.1111/rssb.2012.75.issue-1
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    Citations

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    Cited by:

    1. 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.
    2. Solari, Aldo & Djordjilović, Vera, 2022. "Multi split conformal prediction," Statistics & Probability Letters, Elsevier, vol. 184(C).
    3. Jana Janková & Rajen D. Shah & Peter Bühlmann & Richard J. Samworth, 2020. "Goodness‐of‐fit testing in high dimensional generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 773-795, July.
    4. Timothy I. Cannings & Richard J. Samworth, 2017. "Random-projection ensemble classification," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 959-1035, September.
    5. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    6. Xiangyu Wang & Chenlei Leng, 2016. "High dimensional ordinary least squares projection for screening variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 589-611, June.
    7. Aurélien Sallin & Simone Balestra, 2022. "The Earth is Not Flat: A New World of High-Dimensional Peer Effects," Economics of Education Working Paper Series 0189, University of Zurich, Department of Business Administration (IBW).
    8. Armeen Taeb & Parikshit Shah & Venkat Chandrasekaran, 2020. "False discovery and its control in low rank estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 997-1027, September.
    9. Juan Armando Torres Munguía, 2024. "A model-based boosting approach to risk factors for physical intimate partner violence against women and girls in Mexico," Journal of Computational Social Science, Springer, vol. 7(2), pages 1937-1963, October.
    10. Sonja Greven & Fabian Scheipl, 2020. "Comments on: Inference and computation with Generalized Additive Models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 343-350, June.
    11. Chun-Xia Zhang & Jiang-She Zhang & Sang-Woon Kim, 2016. "PBoostGA: pseudo-boosting genetic algorithm for variable ranking and selection," Computational Statistics, Springer, vol. 31(4), pages 1237-1262, December.
    12. D García Rasines & G A Young, 2023. "Splitting strategies for post-selection inference," Biometrika, Biometrika Trust, vol. 110(3), pages 597-614.
    13. Lu Lin & Feng Li, 2023. "Global debiased DC estimations for biased estimators via pro forma regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 726-758, June.
    14. Huang, Shih-Ting & Xie, Fang & Lederer, Johannes, 2021. "Tuning-free ridge estimators for high-dimensional generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    15. Panxu Yuan & Yinfei Kong & Gaorong Li, 2024. "FDR control and power analysis for high-dimensional logistic regression via StabKoff," Statistical Papers, Springer, vol. 65(5), pages 2719-2749, July.
    16. Sohrabi, Narges & Movaghari, Hadi, 2020. "Reliable factors of Capital structure: Stability selection approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 296-310.
    17. Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 1-40, March.
    18. Jacob Bien & Irina Gaynanova & Johannes Lederer & Christian L. Müller, 2019. "Prediction error bounds for linear regression with the TREX," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 451-474, June.
    19. Capanu, Marinela & Giurcanu, Mihai & Begg, Colin B. & Gönen, Mithat, 2023. "Subsampling based variable selection for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    20. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
    21. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
    22. Ron Ammar & Pitchumani Sivakumar & Gabor Jarai & John Ryan Thompson, 2019. "A robust data-driven genomic signature for idiopathic pulmonary fibrosis with applications for translational model selection," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-15, April.
    23. Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 59-67, March.
    24. Emma Schwager & Himel Mallick & Steffen Ventz & Curtis Huttenhower, 2017. "A Bayesian method for detecting pairwise associations in compositional data," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-21, November.
    25. Gillard, Jonathan & Zhigljavsky, Anatoly, 2018. "Optimal estimation of direction in regression models with large number of parameters," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 281-289.

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