A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications. (28th April 2020)

• Record Type: Journal Article
• Authors: Kibria, B. M. Golam; Lukman, Adewale F.
• Other Names: Kucuk Osman Academic Editor.
• Is Part Of: Scientifica. Volume 2020(2020)
• Publication Date: 2020-04-28
• Subjects: Life sciences -- Periodicals; Biology -- Periodicals; Medicine -- Periodicals; Biological Science Disciplines; Medicine; Biology; Life sciences; Medicine; Periodicals; Electronic journals; Periodicals; 500
• DOI: 10.1155/2020/9758378
• Languages: English
• ISSNs: 2090-908X

Abstract

Abstract : The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model. Theory and simulation results show that, under some conditions, it performs better than both Liu and ridge regression estimators in the smaller MSE sense. Two real-life (chemical and economic) data are analyzed to illustrate the findings of the paper.