A new ridge-type estimator for the linear regression model with correlated regressors

The ridge-type regression estimators are frequently being used to address multicollinearity in the linear regression model due to the inefficiency of the famous ordinary least squares estimator. The ridge-type regression estimator can be in one or two-parameter form. This paper proposes another ridge-type estimator, a two-parameter ridge-type estimator, and establishes its statistical properties theoretically and through Monte Carlo simulation studies. Two different biasing parameters (k(1)d(1) and k(2)d(2)) were considered for the proposed and compared with six other existing estimators. Results of Monte Carlo simulation studies show the dominance of the proposed method over some existing ones using the mean squared criterion. In addition, the proposed dominated the existing estimators when applied to real-life datasets using mean squared error and cross-validation as the criterion.