Alternative ridge parameters in linear model

Abstract

The ridge regression estimator produces efficient estimates than the Ordinary Least Square Estimator in a linear regression model that has multicollinearity problem. However, the efficiency of the ridge estimator depends on the choice of the ridge parameter, k. The ridge parameters are classified into different forms, various types, and diverse kinds. These classifications resulted in proposing some other techniques of Ridge parameter estimation. Investigation of the existing and proposed ridge parameters was done by conducting Monte-Carlo experiments. Results from the simulation study and real-life data application show that some newly proposed ridge parameters are among those that provide efficient estimates.