A new ridge type estimator and its performance for the linear regression model: Simulation and application
Sohail Chand, B. M. Golam Kibria
Abstract
Ridge regression is employed to address the issue of multicollinearity among independent variables. The shrinkage parameter (k) plays a key role in balancing the bias and variance tradeoff. This paper reviewed several promising existing ride regression estimators designed for estimating the ridge or shrinkage parameter k within the Gaussian linear regression model. In addition, we have proposed a new estimator (CK), which is a function of number of independent variables, sample size and standard error of regression model. The performance of our proposed estimator with OLS and existing shrinkage estimators, is compared using extensive Monte Carlo simulations in terms of minimum mean squared error (MSE). Simulation results demonstrated that the proposed CK estimator outperformed other in the majority of the considered simulation scenarios. A real-life data is analyzed to illustrate the findings of the paper.