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All issues Volume 43 / No 3 (June 2025) JNWPU, 43 3 (2025) 620-629 References
Open Access
Issue
JNWPU
Volume 43, Number 3, June 2025
Page(s) 620 - 629
DOI https://doi.org/10.1051/jnwpu/20254330620
Published online 11 August 2025
  1. ZHOU Zhihua. Machine learning[M]. Beijing: Tsinghua University Press, 2016 (in Chinese) [Google Scholar]
  2. HU Qinghua, WANG Yun. A review of robust regression modeling approaches with noise[J]. Journal of Northwest University, 2019, 49(4): 496–507 (in Chinese) [Google Scholar]
  3. FAN J Q, WANG W C, ZHU Z W. A shrinkage principle for heavy-tailed data: high-dimensional robust low-rank matrix recovery[J]. Annals of Statistics, 2021, 49(3): 1239–1266 [Google Scholar]
  4. GUO W, WANG Z, DU W L. Robust semi-supervised multi-view graph learning with sharable and individual structure[J]. Pattern Recognition, 2023, 140: 109565 [Google Scholar]
  5. WANG H S, LI G D, JIANG G H. Robust regression shrinkage and consistent variable selection through the LAD-LASSO[J]. Journal of Business and CMD Economie, 2007, 25(3): 347–355 [Google Scholar]
  6. KONERKER R. Quantile regression[M]. Cambridge: Cambridge University Press, 2005 [Google Scholar]
  7. YI C R, HUANG J. Semismooth newton coordinate descent algorithm for elastic net penalized Huber loss regression and quantile regression[J]. Journal of Computational and Graphical Statistics, 2017, 26(3): 547–557 [Google Scholar]
  8. SU M H, WANG W J. A network LASSO model for regression[J]. Communications in Statistics-Theory and Methods, 2023, 52(6): 1702–1727 [Google Scholar]
  9. SU Meihong. Elastic net regression for network data[J]. Journal of Shanxi University, 2023, 46(3): 604–616 (in Chinese) [Google Scholar]
  10. TIBSHITANI R. Regression shrinkage and selection via the LASSO[J]. Journal of the Royal Statistical Society, Series B, 1996, 58(1): 267–288 [Google Scholar]
  11. XU Z B, ZHANG H, WANG Y, et al. L1/2 regularization[J]. Science China Information Science, 2010, 53(6): 1159–1169 [Google Scholar]
  12. ZOU H. The adaptive LASSO and its oracle properties[J]. Journal of the American Statistical Association, 2006, 101(476): 1418–1429 [CrossRef] [Google Scholar]
  13. ZOU H, HASTIE T. Regularization and variable selection via the elastic net[J]. Journal of the Royal Statistical Society, 2005, 67(2): 301–320 [CrossRef] [Google Scholar]
  14. MIN W W, XU T S, DING C. Weighted sparse partial least squares for joint sample and feature selection[J]. IEEE Trans on Knowledge and Data Engineering, 2023: 1–12 [Google Scholar]
  15. SUN Q, ZHOU W X, FAN J Q. Adaptive Huber regression[J]. Journal of the American Statistical Association, 2020, 115(529): 254–265 [Google Scholar]
  16. HAVID H, LESKOVEC J, BOYD S. Network LASSO: Clustering and optimization in large graphs[C]//Proceeding of the 21th ACM SIGKDD International Conference on Knowledge Discovering and Data Mining, 2015 [Google Scholar]
  17. ZHU Q Y, QIN A K, ABEYSEKARA P, et al. Decentralised traffic incident detection via network LASSO[J/OL](2024-02-28)[2024-04-10]. [Article] [Google Scholar]

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