Comparing Ridge Regression Estimators: Exploring Both New and Old Methods

Ridge regression presents a method to tackle multicollinearity issues. Several estimators and predictors for the estimation of biasing parameter k have been extensively detailed in scholarly literature. We offer a thorough analysis of both conventional and emerging methods aimed at precisely determining the ridge parameter k. Our investigation provides valuable insights into the properties of these estimators and their practical efficacy in various applications. Proposed estimators for the parameter k are assessed using Monte Carlo simulations and a real-world example, with a focus on evaluating their performance based on Mean Squared Error (MSE). Our estimator, in conjunction with others, showcases commendable performance, as indicated by the results.