A new hybrid estimator for linear regression model analysis: Computations and simulations

The Linear regression model explores the relationship between a response variable and one or more independent variables. The parameters in the model are often estimated using the Ordinary Least Square Estimator (OLSE). However, OLSE suffers a breakdown when there is linear dependency among the predictors-a condition called multicollinearity. Several alternative estimators have been suggested as replacements for the OLSE. These include the Kibria-Lukman estimator and the modified ridge-type estimator. In this study, we pro-posed a hybrid estimator by combining the Kibria-Lukman estimator with the modified ridge-type estimator. The proposed estimator theoretically dominates the existing estima-tors. The simulation studies and real-life application supports the theoretical findings.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )