[1] Hoerl, E. and Kennard, W. (1970) Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12, 55-67.
http://dx.doi.org/10.1080/00401706.1970.10488634
[2] Liu, K. (1993) A New Class of Biased Estimate in Linear Regression. Communications in Statistics—Theory and Methods, 22, 393-402.
http://dx.doi.org/10.1080/03610929308831027
[3] Akdeniz, F. and Kaçiranlar, S. (1995) On the almost Unbiased Generalized Liu Estimator and Unbiased Estimation of the Bias and MSE. Communications in Statistics—Theory and Methods, 34, 1789-1797.
http://dx.doi.org/10.1080/03610929508831585
[4] Theil, H. and Goldberger. A.S. (1961) On Pure and Mixed Estimation in Economics. International Economic Review, 2, 65-77.
http://dx.doi.org/10.2307/2525589
[5] Hubert, M.H. and Wijekoon, P. (2006) Improvement of the Liu Estimator in Linear Regression Model. Statistical Papers, 47, 471-479.
http://dx.doi.org/10.1007/s00362-006-0300-4
[6] Bancroft, A. (1944) On Biases in Estimation Due to Use of Preliminary Tests of Significance. Annals of Mathematical Statistics, 15, 190-204.
http://dx.doi.org/10.1214/aoms/1177731284
[7] Judge, G. and Bock, E. (1978) The Statistical Implications of Pre-Test and Stein-Rule Estimators in Econometrics. North Holland, New York.
[8] Wijekoon, P. (1990) Mixed Estimation and Preliminary Test Estimation in the Linear Regression Model. Ph.D. Thesis, University of Dortmund, Dortmund.
[9] Arumairajan, S. and Wijekoon, P. (2013) Improvement of the Preliminary Test Estimator When Stochastic Restrictions are Available in Linear Regression Model. Open Journal of Statistics, 3, 283-292.
http://dx.doi.org/10.4236/ojs.2013.34033
[10] Gruber. M.H.J. (1998) Improving Efficiency by Shrinkage: The James-Stein and Ridge Regression Estimators. Dekker, Inc., New York.
[11] Akdeniz, F. and Erol, H. (2003) Mean Squared Error Matrix Comparisons of Some Biased Estimators in Linear Regression. Communications in Statistics—Theory and Methods, 32, 2389-2413.
http://dx.doi.org/10.1081/STA-120025385
[12] Li, Y. and Yang, H. (2010) A New Stochastic Mixed Ridge Estimator in Linear Regression. Statistical Papers, 51, 315-323.
http://dx.doi.org/10.1007/s00362-008-0169-5
[13] Wu, J. and Yang, H. (2013) Two Stochastic Restricted Principal Components Regression Estimator in Linear Regression. Communications in Statistics—Theory and Methods, 42, 3793-3804.
http://dx.doi.org/10.1080/03610926.2011.639004
[14] McDonald, C. and Galarneau, A. (1975) A Monte Carlo Evaluation of some Ridge-Type Estimators. Journal of American Statistical Association, 70, 407-416.
http://dx.doi.org/10.1080/01621459.1975.10479882
[15] Newhouse, J.P. and Oman, S.D. (1971) An Evaluation of Ridge Estimators. Rand Report, No. R-716-Pr, 1-28.
[16] Rao, C.R. and Touterburg, H. (1995) Linear Models, Least Squares and Alternatives. Springer Verlag, Berlin.
http://dx.doi.org/10.1007/978-1-4899-0024-1
[17] Farebrother, R.W. (1976) Further Results on the Mean Square Error of Ridge Regression. Journal of the Royal Statistical Society, 38, 248-250.
[18] Wang, S.G., Wu, M.X. and Jia, Z.Z. (2006) Matrix Inequalities. 2nd Edition, Chinese Science Press, Beijing.
[19] Trenkler, G. and Toutenburg, H. (1990) Mean Square Error Matrix Comparisons between Biased Estimators—An Overview of Recent Results. Statistical Papers, 31, 165-179.
http://dx.doi.org/10.1007/BF02924687