A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications
B. M. Golam Kibria, Adewale F. Lukman
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.
Topics & Concepts
MulticollinearityEstimatorLinear regressionRidgeRegression analysisMathematicsRegressionStatisticsLinear modelProper linear modelVariance inflation factorNonlinear regressionEconometricsPolynomial regressionGeologyPaleontologyAdvanced Statistical Methods and ModelsSpectroscopy and Chemometric AnalysesAdvanced Statistical Process Monitoring