New robust ridge estimators for the linear regression model with outliers

References

• Alkhamisi, M. A., and G. Shukur. 2007. A Monte Carlo study of recent ridge parameters. Communications in Statistics – Simulation and Computation 36 (3):535–47. doi: 10.1080/03610910701208619.
   Web of Science ®Google Scholar
• Alkhamisi, M. A., G. Khalaf, and G. Shukur. 2006. Some modifications for choosing ridge parameter. Communication in Statistics – Theory and Methods 35:1–16.
   Web of Science ®Google Scholar
• Amin, M., M. N. Akram, and A. Majid. 2021. On the estimation of Bell regression model using ridge estimator. Communications in Statistics – Simulation and Computation. Advance online publication. doi: 10.1080/03610918.2020.1870694.
   Web of Science ®Google Scholar
• Dorugade, A. V. 2014. New ridge parameters for ridge regression. Journal of the Association of Arab Universities for Basic and Applied Sciences 15 (1):94–99. doi: 10.1016/j.jaubas.2013.03.005.
   Google Scholar
• Ertaş, H. 2018. A modified ridge m-estimator for linear regression model with multicollinearity and outliers. Communications in Statistics - Simulation and Computation 47 (4):1240–50. doi: 10.1080/03610918.2017.1310231.
   Web of Science ®Google Scholar
• Gibbons, D. G. 1981. A simulation study of some ridge estimators. Journal of the American Statistical Association 76 (373):131–39. doi: 10.1080/01621459.1981.10477619.
   Web of Science ®Google Scholar
• Gültay, B., and S. Kaçıranlar. 2015. Mean square error comparisons of the alternative estimators for the distributed lag models. Hacettepe Journal of Mathematics and Statistics 44 (35):1–33. doi: 10.15672/HJMS357556.
   Google Scholar
• Hampel, F. R., E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel. 1986. Robust statistics: The approach based on influence function. New York: Wiley.
   Google Scholar
• Hoerl, A. E., and R. W. Kennard. 1970. Ridge regression: Biased estimation for non-orthogonal problems. Technometrics 12 (1):55–67. doi: 10.1080/00401706.1970.10488634.
   Web of Science ®Google Scholar
• Hoerl, A. E., R. W. Kennard, and K. F. Baldwin. 1975. Ridge regression: Some simulation. Communications in Statistics – Simulation and Computation 4 (2):105–23. doi: 10.1080/03610917508548342.
   Web of Science ®Google Scholar
• Huber, P. J. 1981. Robust statistics. New York: Wiley.
   Google Scholar
• Khalaf, G., and G. Shukur. 2005. Choosing ridge parameter for regression problem. Communications in Statistics – Theory and Methods 34 (5):1177–82. doi: 10.1081/STA-200056836.
   Web of Science ®Google Scholar
• Kibria, B. M. G. 2003. Performance of some new ridge regression estimators. Communications in Statistics – Simulation and Computation 32 (2):419–35. doi: 10.1081/SAC-120017499.
   Web of Science ®Google Scholar
• Lawless, J. F., and P. Wang. 1976. A simulation study of ridge and other regression estimators. Communication in Statistics – Theory and Methods 14:1589–604.
   Google Scholar
• Majid, A., M. Aslam, and S. Altaf. 2018. Efficient estimation of distributed lag model in presence of heteroscedasticity of unknown form: A Monte Carlo evidence. Cogent Mathematics & Statistics 5 (1):1538596– 12. doi: 10.1080/25742558.2018.1538596.
   Web of Science ®Google Scholar
• Månsson, K., and G. Shukur. 2011. On ridge parameters in logistic regression. Communications in Statistics – Theory and Methods 40 (18):3366–81. doi: 10.1080/03610926.2010.500111.
   Web of Science ®Google Scholar
• Månsson, K., G. Shukur, and B. M. G. Kibria. 2010. A simulation study of some ridge regression estimators under different distributional assumptions. Communications in Statistics – Simulation and Computation 39 (8):1639–70. doi: 10.1080/03610918.2010.508862.
   Web of Science ®Google Scholar
• McDonald, G. C., and D. I. Galarneau. 1975. A Monte Carlo evaluation of some Ridge-type estimators. Journal of the American Statistical Association 70 (350):16–407. doi: 10.1080/01621459.1975.10479882.
   Web of Science ®Google Scholar
• Muniz, G., and B. M. G. Kibria. 2009. On some ridge regression estimators: An empirical comparisons. Communications in Statistics – Simulation and Computation 38 (3):621–30. doi: 10.1080/03610910802592838.
   Web of Science ®Google Scholar
• Myers, R. H. 1990. Classical and modern regression with applications. Belmont, CA: Duxbury Press.
   Google Scholar
• Newhouse, J. P., and S. D. Oman. 1971. An evaluation of ridge estimators. Report No. R-716-PR. Santa Monica, CA: Rand Corporation, pp. 1–16.
   Google Scholar
• Qasim, M., M. Amin, and T. Omer. 2020. Performance of some new Liu parameters for the linear regression model. Communications in Statistics – Theory and Methods 49 (17):4178–96. doi: 10.1080/03610926.2019.1595654.
   Web of Science ®Google Scholar
• Rousseeuw, P. J., and A. M. Leroy. 1987. Robust regression and outliers detection. New York: Wiley.
   Google Scholar
• Sami, F., M. Amin, and M. M. Butt. 2021. On the ridge estimation of the Conway-Maxwell Poisson regression model with multicollinearity: Methods and applications. Concurrency and Computation: Practice and Experience. Advance online publication. doi: 10.1002/cpe.6477.
   Web of Science ®Google Scholar
• Silvapulle, M. J. 1991. Robust ridge regression based on an M-estimator. Australian Journal of Statistics 33 (3):319–33. doi: 10.1111/j.1467-842X.1991.tb00438.x.
   Google Scholar
• Suhail, M., S. Chand, and B. M. G. Kibria. 2020. Quantile based estimation of biasing parameters in ridge regression model. Communications in Statistics – Simulation and Computation 49 (10):2732–44. doi: 10.1080/03610918.2018.1530782.
   Web of Science ®Google Scholar