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Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection

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  1. Challet, Damien & Bongiorno, Christian & Pelletier, Guillaume, 2021. "Financial factors selection with knockoffs: Fund replication, explanatory and prediction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
  2. Srinivasan, Arun & Xue, Lingzhou & Zhan, Xiang, 2023. "Identification of microbial features in multivariate regression under false discovery rate control," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
  3. Dong, Yan & Li, Daoji & Zheng, Zemin & Zhou, Jia, 2022. "Reproducible feature selection in high-dimensional accelerated failure time models," Statistics & Probability Letters, Elsevier, vol. 181(C).
  4. Laura Freijeiro‐González & Manuel Febrero‐Bande & Wenceslao González‐Manteiga, 2022. "A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates," International Statistical Review, International Statistical Institute, vol. 90(1), pages 118-145, April.
  5. Emmanuel Candès & Chiara Sabatti, 2020. "Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 60-63, December.
  6. Yumei Ren & Guoqiang Tang & Xin Li & Xuchang Chen, 2023. "A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression," Mathematics, MDPI, vol. 11(16), pages 1-20, August.
  7. D García Rasines & G A Young, 2023. "Splitting strategies for post-selection inference," Biometrika, Biometrika Trust, vol. 110(3), pages 597-614.
  8. Pedro Delicado & Daniel Peña, 2023. "Understanding complex predictive models with ghost variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 107-145, March.
  9. Emre Demirkaya & Yang Feng & Pallavi Basu & Jinchi Lv, 2022. "Large-scale model selection in misspecified generalized linear models [Information theory and an extension of the maximum likelihood principle]," Biometrika, Biometrika Trust, vol. 109(1), pages 123-136.
  10. Azadkia, Mona & Chatterjee, Sourav, 2021. "A simple measure of conditional dependence," LSE Research Online Documents on Economics 125584, London School of Economics and Political Science, LSE Library.
  11. Rajchert, Andrew & Keich, Uri, 2023. "Controlling the false discovery rate via competition: Is the +1 needed?," Statistics & Probability Letters, Elsevier, vol. 197(C).
  12. Tian, Zhentao & Zhang, Zhongzhan, 2025. "Quantile feature screening for infinite dimensional data under FDR control," Computational Statistics & Data Analysis, Elsevier, vol. 206(C).
  13. 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.
  14. L Bottolo & S Richardson, 2019. "Discussion of ‘Gene hunting with hidden Markov model knockoffs’," Biometrika, Biometrika Trust, vol. 106(1), pages 19-22.
  15. Yi Liu & Veronika Ročková & Yuexi Wang, 2021. "Variable selection with ABC Bayesian forests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 453-481, July.
  16. Xie, Zilong & Chen, Yunxiao & von Davier, Matthias & Weng, Haolei, 2023. "Variable selection in latent variable models via knockoffs: an application to international large-scale assessment in education," LSE Research Online Documents on Economics 120812, London School of Economics and Political Science, LSE Library.
  17. Shi, Chengchun & Xu, Tianlin & Bergsma, Wicher & Li, Lexin, 2021. "Double generative adversarial networks for conditional independence testing," LSE Research Online Documents on Economics 112550, London School of Economics and Political Science, LSE Library.
  18. Baihua He & Di Xia & Yingli Pan, 2024. "High dimensional controlled variable selection with model-X knockoffs in the AFT model," Computational Statistics, Springer, vol. 39(4), pages 1993-2009, June.
  19. Pan, Yingli, 2022. "Feature screening and FDR control with knockoff features for ultrahigh-dimensional right-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
  20. Subhadeep Mukhopadhyay, 2021. "InfoGram and Admissible Machine Learning," Papers 2108.07380, arXiv.org, revised Aug 2021.
  21. Małgorzata Łazȩcka & Bartosz Kołodziejek & Jan Mielniczuk, 2023. "Analysis of conditional randomisation and permutation schemes with application to conditional independence testing," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(4), pages 1459-1478, December.
  22. Zihuai He & Linxi Liu & Michael E. Belloy & Yann Guen & Aaron Sossin & Xiaoxia Liu & Xinran Qi & Shiyang Ma & Prashnna K. Gyawali & Tony Wyss-Coray & Hua Tang & Chiara Sabatti & Emmanuel Candès & Mich, 2022. "GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
  23. Wen, Xin & Li, Yang & Zheng, Zemin, 2024. "Scalable efficient reproducible multi-task learning via data splitting," Statistics & Probability Letters, Elsevier, vol. 208(C).
  24. Dae Woong Ham & Jiaze Qiu, 2023. "Hypothesis testing in adaptively sampled data: ART to maximize power beyond iid sampling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 998-1037, September.
  25. Adel Javanmard & Jason D. Lee, 2020. "A flexible framework for hypothesis testing in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 685-718, July.
  26. Arun Srinivasan & Lingzhou Xue & Xiang Zhan, 2021. "Compositional knockoff filter for high‐dimensional regression analysis of microbiome data," Biometrics, The International Biometric Society, vol. 77(3), pages 984-995, September.
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