Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary least squares (OLS) estimation in the case of highly intercorrelated explanatory variables in the linear regression model Y = β + u. Two proposed ridge regression parameters from the mean square error (MSE) perspective are evaluated. A simulation study was conducted to demonstrate the performance of the proposed estimators compared to the OLS, HK and HKB estimators. Results show that the suggested estimators outperform the OLS and the other estimators regarding the ridge parameters in all situations examined.
"A Proposed Ridge Parameter to Improve the Least Square Estimator,"
Journal of Modern Applied Statistical Methods:
2, Article 15.
Available at: http://digitalcommons.wayne.edu/jmasm/vol11/iss2/15