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Strong rules for discarding predictors in lasso‐type problems

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Cited by:

  1. Jie Xiong & Zhitong Bing & Yanlin Su & Defeng Deng & Xiaoning Peng, 2014. "An Integrated mRNA and microRNA Expression Signature for Glioblastoma Multiforme Prognosis," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
  2. Jason Poulos & Shuxi Zeng, 2021. "RNN‐based counterfactual prediction, with an application to homestead policy and public schooling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1124-1139, August.
  3. Ottoboni Kellie N. & Poulos Jason V., 2020. "Estimating population average treatment effects from experiments with noncompliance," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 108-130, January.
  4. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
  5. Liao Zhu & Sumanta Basu & Robert A. Jarrow & Martin T. Wells, 2020. "High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 1-52, December.
  6. Barbaglia, Luca & Wilms, Ines & Croux, Christophe, 2016. "Commodity dynamics: A sparse multi-class approach," Energy Economics, Elsevier, vol. 60(C), pages 62-72.
  7. Arulsamy, Karen & Delaney, Liam, 2022. "The impact of automatic enrolment on the mental health gap in pension participation: Evidence from the UK," Journal of Health Economics, Elsevier, vol. 86(C).
  8. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
  9. Mohamed Ouhourane & Yi Yang & Andréa L. Benedet & Karim Oualkacha, 2022. "Group penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 495-529, September.
  10. Can Wu & Ying Cui & Donghui Li & Defeng Sun, 2023. "Convex and Nonconvex Risk-Based Linear Regression at Scale," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 797-816, July.
  11. Yen, Tso-Jung & Yen, Yu-Min, 2016. "Structured variable selection via prior-induced hierarchical penalty functions," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 87-103.
  12. Liao Zhu & Robert A. Jarrow & Martin T. Wells, 2021. "Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-30, December.
  13. Chen, Huangyue & Kong, Lingchen & Shang, Pan & Pan, Shanshan, 2020. "Safe feature screening rules for the regularized Huber regression," Applied Mathematics and Computation, Elsevier, vol. 386(C).
  14. Kellie Ottoboni & Jason Poulos, 2019. "Estimating population average treatment effects from experiments with noncompliance," Papers 1901.02991, arXiv.org, revised Aug 2020.
  15. Zeng, Yaohui & Yang, Tianbao & Breheny, Patrick, 2021. "Hybrid safe–strong rules for efficient optimization in lasso-type problems," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
  16. Yongxiu Cao & Jian Huang & Yanyan Liu & Xingqiu Zhao, 2016. "Sieve estimation of Cox models with latent structures," Biometrics, The International Biometric Society, vol. 72(4), pages 1086-1097, December.
  17. Julien Hambuckers & Li Sun & Luca Trapin, 2023. "Measuring tail risk at high-frequency: An $L_1$-regularized extreme value regression approach with unit-root predictors," Papers 2301.01362, arXiv.org.
  18. Cristofari, Andrea, 2023. "A decomposition method for lasso problems with zero-sum constraint," European Journal of Operational Research, Elsevier, vol. 306(1), pages 358-369.
  19. Allimuthu Elangovan & Nguyen Trung Duc & Dhandapani Raju & Sudhir Kumar & Biswabiplab Singh & Chandrapal Vishwakarma & Subbaiyan Gopala Krishnan & Ranjith Kumar Ellur & Monika Dalal & Padmini Swain & , 2023. "Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice," Agriculture, MDPI, vol. 13(4), pages 1-22, April.
  20. Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2021. "Solving a class of feature selection problems via fractional 0–1 programming," Annals of Operations Research, Springer, vol. 303(1), pages 265-295, August.
  21. Gross, Samuel M. & Tibshirani, Robert, 2016. "Data Shared Lasso: A novel tool to discover uplift," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 226-235.
  22. Gabriel E Hoffman & Benjamin A Logsdon & Jason G Mezey, 2013. "PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data," PLOS Computational Biology, Public Library of Science, vol. 9(6), pages 1-19, June.
  23. Juan Carlos Laria & Line H. Clemmensen & Bjarne K. Ersbøll & David Delgado-Gómez, 2022. "A Generalized Linear Joint Trained Framework for Semi-Supervised Learning of Sparse Features," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
  24. Michoel, Tom, 2016. "Natural coordinate descent algorithm for L1-penalised regression in generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 60-70.
  25. Ana R. Leal & David Perez-Castillo & José Ernesto Amorós & Bryan W. Husted, 2020. "Municipal Green Purchasing in Mexico: Policy Adoption and Implementation Success," Sustainability, MDPI, vol. 12(20), pages 1-26, October.
  26. David Degras, 2021. "Sparse group fused lasso for model segmentation: a hybrid approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 625-671, September.
  27. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
  28. Guo, Yi & Berman, Mark & Gao, Junbin, 2014. "Group subset selection for linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 39-52.
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