WSEAS Transactions on Mathematics
Print ISSN: 1109-2769, E-ISSN: 2224-2880
Volume 21, 2022
Predictive Performance Evaluation of the Kibria-Lukman Estimator
Authors: , ,
Abstract: Regression models are commonly used in prediction, but their predictive performances may be affected by the problem called the multicollinearity. To reduce the effect of the multicollinearity, different biased estimators have been proposed as alternatives to the ordinary least squares estimator. But there are still little analyses of the different proposed biased estimators’ predictive performances. Therefore, this paper focuses on discussing the predictive performance of the recently proposed “new ridge-type estimator”, namely the Kibria-Lukman (KL) estimator. The theoretical comparisons among the predictors of these estimators are done according to the prediction mean squared error criterion in the two-dimensional space and the results are explained by a numerical example. The regions are determined where the KL estimator gives better results than the other estimators.
Search Articles
Keywords: Biased Estimator, Ridge Estimator, Liu Estimator, Kibria-Lukman estimator, Prediction Mean Square Error, Multicollinearity
Pages: 641-649
DOI: 10.37394/23206.2022.21.75