Performance of Some New Ridge Parameters in Two-Parameter Ridge Regression Model

Two-parameter ridge regression is a widely used method in the last two decades to circumvent the problem of multicollinearity. Ridge parameter k plays an important role in such situations. Several methods are available in the literature for the estimation of ridge parameter. For high multicollinearity, the available methods do not perform well in terms of mean square error. In this article, we propose some new estimators for the ridge parameter. Based on simulation study, our new estimators generally perform better than ordinary least squares estimator, ridge regression estimator and two-parameter ridge regression estimator in many considered scenarios especially for high multicollinearity. In addition, the new estimators also perform well for some non-normal error distributions. Finally, two real-life examples are used to illustrate the application of the proposed estimator.

Automated summary

This article proposes new estimators for the ridge parameter to address the issue of multicollinearity, showing better performance in simulation studies, especially for high multicollinearity scenarios. The new estimators are also effective for some non-normal error distributions.