Generalized Kibria-Lukman Estimator: Method, Simulation, and Application

In the linear regression model, multicollinearity effects on ordinary least squares (OLS) estimator performance make it inefficient. To solve this, several estimators are given. The Kibria-Lukman (KL) is a recent that has been proposed to problem. this paper, generalized version of KL proposed, along with optimal biasing parameter our derived by minimizing scalar mean squared error. Theoretically, compared OLS, ridge, Liu, and matrix Furthermore, simulation study numerical example were performed for comparing OLS estimators. results indicate better than other estimators, especially in cases where standard deviation errors was large when correlation between explanatory variables very high.