[24] John W Hilgers. On the equivalence of regularization and certain reproducing kernel Hilbert space approaches for solving first kind problems. SIAM Journal on Numerical Analysis, 13(2): 172-184, 1976.
[25] Arthur E Hoerl and Robert W Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1):55-67, 1970.
[26] Daniel Hsu, Sham M. Kakade, and Tong Zhang. Random design analysis of ridge regression. In Shie Mannor, Nathan Srebro, and Robert C. Williamson, editors, Proceedings of the 25th Annual Conference on Learning Theory, volume 23 of Proceedings of Machine Learning Research, pages 9.1–9.24, Edinburgh, Scotland, 25–27 Jun 2012. PMLR.
[27] W JAMES. Estimation with quadratic loss. In Proc. 4th Berkeley Symp. on Math. Statist. and Prob., 1961, 1961.
[28] Lawless JF. A simulation study of ridge and other regression estimators. *Communications in Statistics-theory and Methods*, 5(4):307-323, 1976.
[29] Ghadban Khalaf and Ghazi Shukur. Choosing ridge parameter for regression problems. 2005.
[30] BM Golam Kibria. Performance of some new ridge regression estimators. Communications in Statistics-Simulation and Computation, 32(2):419-435, 2003.
[31] Dmitry Kobak, Jonathan Lomond, and Benoit Sanchez. The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization. The Journal of Machine Learning Research, 21(1):6863-6878, 2020.
[32] D. V. Lindley and A. F. M. Smith. Bayes estimates for the linear model. Journal of the Royal Statistical Society (Series B), 34(1):1-41, 1972.
[33] Colin L Mallows. Some comments on Cp. Technometrics, 42(1):87-94, 2000.
[34] Gisela Muniz and BM Golam Kibria. On some ridge regression estimators: An empirical comparisons. Communications in Statistics—Simulation and Computation® 38(3):621-630, 2009.
[35] Trevor Park and George Casella. The Bayesian lasso. Journal of the American Statistical Association, 103(482):681-686, 2008.
[36] Pratik Patil, Yuting Wei, Alessandro Rinaldo, and Ryan Tibshirani. Uniform consistency of cross-validation estimators for high-dimensional ridge regression. In International Conference on Artificial Intelligence and Statistics, pages 3178-3186. PMLR, 2021.
[37] François Petitjean, Jordi Inglada, and Pierre Gançarski. Satellite image time series analysis under time warping. IEEE transactions on geoscience and remote sensing, 50(8):3081-3095, 2012.
[38] Nicholas G Polson and James G Scott. On the half-Cauchy prior for a global scale parameter. Bayesian Analysis, 7(4):887-902, 2012.
[39] Todd E Scheetz, Kwang-Youn A Kim, Ruth E Swiderski, Alisdair R Philp, Terry A Braun, Kevin L Knudtson, Anne M Dorrance, Gerald F DiBona, Jian Huang, Thomas L Casavant, et al. Regulation of gene expression in the mammalian eye and its relevance to eye disease. Proceedings of the National Academy of Sciences, 103(39):14429-14434, 2006.
[40] Gideon Schwarz et al. Estimating the dimension of a model. The annals of statistics, 6(2): 461-464, 1978.
[41] David Spiegelhalter, Andrew Thomas, Nicky Best, and Wally Gilks. BUGS 0.5: Bayesian inference using gibbs sampling manual (version ii). MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK, pages 1-59, 1996.
[42] Will Stephenson, Zachary Frangella, Madeleine Udell, and Tamara Broderick. Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression. Advances in Neural Information Processing Systems, 34:24352-24364, 2021.