%0 Journal Article
%T Improving the Ordinary Least Squares Estimator by Ridge Regression
%A Ghadban Khalaf
%J Open Access Library Journal
%V 9
%N 5
%P 1-8
%@ 2333-9721
%D 2022
%I Open Access Library
%R 10.4236/oalib.1108738
%X In the presence of multicollinearity, ridge regression techniques result in estimated coefficients that are biased but have smaller variance than Ordinary Least Squares estimators and may, therefore, have a smaller Mean Squares Error (MSE). The ridge solution is to supplement the data by stochastically shrinking the estimates toward zero. In this study, we propose a new estimator to reduce the effect of multicollinearity and improve the estimation. We show by a simulation study that the MSE of the suggested estimator is lower than other estimators of the ridge and the OLS estimators.
%K OLS Estimator
%K Multicollinearity
%K Ridge Regression
%K Simulation
%U http://www.oalib.com/paper/6773618