1. Alfons, A., Croux, C. and Gelper, S., Sparse least trimmed squares regression for analyzing high dimensional large data set, Ann. Appl. Stat. 7 (2013), 226–248.
2. Amini M. and Roozbeh, M., Optimal partial ridge estimation in restricted semiparametric regression models, J. Multivar. Anal. 136 (2015), 26–40.
3. Arashi M., Roozbeh, M., Hamzah, N.A. and Gasparini, M., Ridge regression and its applications in genetic studies, PLoS ONE 16(4) (2021), e0245376.
4. Bertsimas, D. and Tsitsiklis, J.N., Introduction to linear optimization, Athena Scientific, Massachusetts, 1997.
5. Buhlmann, P., Kalisch, M. and Meier, L., High-dimensional statistics with a view towards applications in biology, Annu. Rev. Stat. Appl. 1 (2014), 255–278.
6. Efron, B. and Hastie, T., Computer age statistical inference, Cambridge University Press, Cambridge, 2017.
7. Roozbeh, M., Babaie-Kafaki, S. and Arashi, M., A class of biased estimators based on QR decomposition, Linear Algebra Appl. 508 (2016), 190–205.
8. Roozbeh, M., Babaie-Kafaki, S. and Naeimi Sadigh, A., A heuristic approach to combat multicollinearity in least trimmed squares regression analysis, Appl. Math. Model. 57 (2018), 105–120.
9. Rousseeuw, P.J. and Leroy, A.M., Robust regression and outlier detection, John Wiley and Sons, New York, 1987.
10. Sheather, S.J., A modern approach to regression with R, Springer, New York, 2009.
11. Tibshirani, R., Regression shrinkage and selection via the LASSO, J. R. Stat. Soc. Ser. B, 58 (1996), 267–288.
12. Watkins, D.S., Fundamentals of matrix computations, John Wiley and Sons, New York, 2002.
