An evaluation of biased estimators of regression coefficients—A simulation study
- 22 Downloads
Different versions of generalized and ordinary ridge estimators and shrinkage estimators of regression coefficients are studied in comparison with least squares estimators using simulations. The results show that some of the biased estimators considered are better than the least squares estimator in general and the improvement is substantial in some cases.
KeywordsMean Square Error Ridge Regression Shrinkage Estimator Ridge Parameter Predictive Mean Square Error
Unable to display preview. Download preview PDF.
- Hoerl, A. E., Kennard R. W. (1976), “Ridge Regression: Iterative Estimation of the Biasing Parameter”, Communications in Statistics, A 5, 77–88.Google Scholar
- Mc Gue, M. K. (1981), “Ridge Regression: Estimation and Prediction” Ph. D. Thesis, University of Minnesota.Google Scholar
- Stein, C. (1960), “Multiple Regression”, Constributions to Probability and Statistics. Essays in Honor of Harald Hotelling I, Standford University Press, 424–443.Google Scholar
- Trenkler, G. (1981), “On a Generalized Iteration Estimator” Computational Statistics, de Gruyter, Berlin-New York, 315–335.Google Scholar
- Vinod, H. D. (1977), “Estimating the Largest Acceptable k and a Confidence Interval for Ridge Regression Parameter”, Presented at the Econometric Society European Meeting, Vienna.Google Scholar