The modified Liu-ridge-type estimator: a new class of biased estimators to address multicollinearity
Muhammad Aslam, Shakeel Ahmad
Abstract
This article proposes another general class of biased estimators which includes some popular estimators as special cases and discusses its properties for multiple linear regression models with the issue of multicollinearity. We proposed the estimator by modifying the Liu estimator (LE) with the use of the existing modified ridge-type estimator and provide its properties. Performance of the proposed estimator is compared with many of the leading estimators, using the mean squared error (MSE) matrix criterion. We conducted an extensive simulation study to illustrate the superiority of the proposed estimator.
Topics & Concepts
MulticollinearityEstimatorMean squared errorMinimum-variance unbiased estimatorMathematicsBias of an estimatorEfficient estimatorInvariant estimatorStatisticsExtremum estimatorTrimmed estimatorClass (philosophy)Minimax estimatorJames–Stein estimatorLinear regressionM-estimatorComputer scienceArtificial intelligenceAdvanced Statistical Methods and ModelsAdvanced Statistical Process MonitoringSpectroscopy and Chemometric Analyses