References
- Roozbeh M, Maanavi M, Mohamed NA. A robust counterpart approach for the ridge estimator to tackle outlier effect in restricted multicollinear regression models. J Stat Comput Simul. 2024;94(2):279–296. doi: 10.1080/00949655.2023.2243361
- Roozbeh M, Hamzah NA. Feasible robust estimator in restricted semiparametric regression models based on the LTS approach. Commun Stat Simul Comput. 2017;46(9):7332–7350. doi: 10.1080/03610918.2016.1236954
- Xia Y, Wang J. Robust regression estimation based on low-dimensional recurrent neural networks. IEEE Trans Neural Netw Learn Syst. 2018;29(12):5935–5946. doi: 10.1109/TNNLS.2018.2814824
- Gad AM, Qura ME. Regression estimation in the presence of outliers: a comparative study. Int J Probab Stat. 2016;5(6):65–72. doi: 10.5539/ijsp.v5n6p65
- Yu C, Yao W. Robust linear regression: a review and comparison. Commun Stat Simul Comput. 2017;46(8):6261–6282. doi: 10.1080/03610918.2016.1202271
- Feng Y, Wu Q. A statistical learning assessment of Huber regression. J Approx Theory. 2022;273:105660. doi: 10.1016/j.jat.2021.105660
- Tong H. Functional linear regression with Huber loss. J Complex. 2023;74:101696. doi: 10.1016/j.jco.2022.101696
- Deutelmoser H, Scherer D, Brenner H, et al. Robust Huber-LASSO for improved prediction of protein, metabolite and gene expression levels relying on individual genotype data. Brief Bioinformatics. 2021;22(4):230. doi: 10.1093/bib/bbaa230
- Shi H, Cao J. Robust functional principal component analysis based on a new regression framework. JABES. 2022;27(3):523–543. doi: 10.1007/s13253-022-00495-1
- Balasundaram S, Prasad SC. Robust twin support vector regression based on Huber loss function. Neural Comput Appl. 2020;32(15):11285–11309. doi: 10.1007/s00521-019-04625-8
- Li S, Cai TT, Li H. Transfer learning for high-dimensional linear regression: prediction, estimation and minimax optimality. J R Stat Soc Ser B: Stat Methodol. 2022;84(1):149–173. doi: 10.1111/rssb.12479
- Lin H, Reimherr M. On hypothesis transfer learning of functional linear models. Stat. 2024;1050:22.
- Tian Y, Feng Y. Transfer learning under high-dimensional generalized linear models. J Am Stat Assoc. 2023;118:2684–2697. doi: 10.1080/01621459.2022.2071278
- Huang J, Wang M, Wu Y. Estimation and inference for transfer learning with high-dimensional quantile regression. arXiv preprint arXiv:2211.14578. 2022.
- Jin J, Yan J, Aseltine RH, et al. Transfer learning with large-scale quantile regression. Technometrics. 2024;1–30. doi: 10.1080/00401706.2024.2315952
- Kuzborskij I, Orabona F. Stability and hypothesis transfer learning. In: International conference on machine learning 2013. p. 942–950.
- Du SS, Koushik J, Singh A, et al. Hypothesis transfer learning via transformation functions. Adv Neural Inf Process Syst. 2017;30.
- Lin H, Reimherr M. Smoothness adaptive hypothesis transfer learning. arXiv preprint arXiv:2402.14966. 2024.
- Hu Q, Zeng P, Lin L. The dual and degrees of freedom of linearly constrained generalized lasso. Comput Stat Data Anal. 2015;86:13–26. doi: 10.1016/j.csda.2014.12.010
- Liu Y, Zeng P, Lin L. Degrees of freedom for regularized regression with Huber loss and linear constraints. Stat Papers. 2021;62(5):2383–2405. doi: 10.1007/s00362-020-01192-2