[1]
Gruber, M.H.J., Improving Efficiency by Shrinkage: The James Stein and Ridge Regression Estimators. Marcel Dekker, Inc.: New York, (1998).
DOI: 10.1201/9780203751220
Google Scholar
[2]
Hoer, A. E., and Kennard, R. W., Ridge Regression: Biased Estimation for Non-orthogonal Problems, Techno metrics, 12, pp.55-67, 1970a.
Google Scholar
[3]
Hoer, A. E., and Kennard, R. W., Ridge Regression: Applications to Non-orthogonal Problems, Techno metrics, 12, pp.69-82, 1970b.
Google Scholar
[4]
Massy, W.F., Principal components regression in exploratory statistical research., JASA, 60, pp.234-266, (1965).
Google Scholar
[5]
Liu, K, A new class of biased estimate in linear regression., Communications in Statistics – Theory and Methods, 22 (2), p.393–402, (1993).
DOI: 10.1080/03610929308831027
Google Scholar
[6]
Huber, P. J., Robust Covariances, Statistical Decision Theory and Related Topics, Vol. 2, eds. Gupta and Moore, Academic Press: New York, pp.165-191, (1977).
DOI: 10.1016/b978-0-12-307560-4.50013-2
Google Scholar
[7]
] Huber, P. J., Robust Statistics, Wiley: New York, (1981).
Google Scholar
[8]
Hampel, F. R., Robust Estimation: A Condensed Partial Survey, Zeitschrift für Wahrscheinlichkeitsthiorie und Verwandte Gebiete, 27, pp.87-104, (1973).
DOI: 10.1007/bf00536619
Google Scholar
[9]
Maronna, R. A., Robust M-estimators of Multivariate Location and Scatter, Annals of Statistics, 4, pp.51-67. (1976).
DOI: 10.1214/aos/1176343347
Google Scholar
[10]
Holland, P. W., Weighted Ridge Regression: Combining Ridge and Robust Regression Methods, NBER Working Paper Series, Working Paper #11, pp.1-19, (1973).
DOI: 10.3386/w0011
Google Scholar
[11]
Pariente, S., and Welsch, R. E., Ridge and Robust Regression Using Parametric Linear Programming, Working Paper, MIT, Alfred P. Sloan School of Management, (1977).
Google Scholar
[12]
Askin, R. G., and Montgomery, D. C., Augmented Robust Estimators, Technometrics, 22, pp.333-341, (1980).
DOI: 10.2307/1268317
Google Scholar
[13]
Pfaffenberger, R. C., and Dielman, T. E., A Comparison of Robust Ridge Estimators, Business Economics Section Proceedings of the American Statistical Association, pp.631-635, (1985).
Google Scholar
[14]
Slivapulle, M.J., Robust Ridge Regression Based on an M-Estimator, aus ral.J. statist. 33(3), pp.319-333, (1991).
Google Scholar
[15]
Gui, Q. M., Duan, Q.T., Zhou, Q.Y., and Guo, J.F., A New Class of Biased Linear Estimators in Deficient-rank Linear Models, Chinese quarterly Journal of Mathematics, 16(1), pp.71-78, (2001).
Google Scholar
[16]
Xia Jie-lai., A Kind of New Biased Estimator of Regression Parameters: Root Root Estimator, Chinese J. Math. Statist. And Appl. Prob, 3, pp.21-30, (1988).
Google Scholar
[17]
Nyquist H., Ridge Type M-estimators,. Linear Statistical Inference, T. Calinski and W. Klonecki, eds., Springer, Berlin. pp.246-258, (1985).
DOI: 10.1007/978-1-4615-7353-1_20
Google Scholar
[18]
Olcay Arslan and Nedret Billor., Robust Liu estimator for regression based on an M-estimator. Journal of Applied Statistics, Vol. 27, pp.1-25, (2000).
DOI: 10.1080/02664760021817
Google Scholar
[19]
Hampel F R, Ronchetti, E.M., Rousseeuw, P.J., and Stahel, W. A., Robust Statistics: The Approach Based on Influence Function. New York: John Wiley, (1986).
DOI: 10.1002/9781118186435
Google Scholar
[20]
Rousseeuw, P. J., and van Zomeren, B. C., Unmasking Multivariate Outliers and Leverage Points, Journal of the American Statistical Association, 85, pp.633-651, (1990).
DOI: 10.1080/01621459.1990.10474920
Google Scholar
[21]
Olcay Arslan and Nedret Billor., Roust Ridge Regression Estimation Biased on the GM-estimators, Jour of Math & Comp. Sci, pp.1-9, (1996).
Google Scholar
[22]
Simpson. J. R. and Montgomeryd.D. C., A biased-robust regression technique for the combined outlier-multicollinearity problem, Journal of statistical computation and simulation, 56(1), pp.1-25, (1996).
DOI: 10.1080/00949659608811777
Google Scholar
