The KL estimator for the inverse Gaussian regression model. (28th February 2021)

• Record Type: Journal Article
• Authors: Lukman, Adewale F.; Algamal, Zakariya Y.; Kibria, B. M. Golam; Ayinde, Kayode
• Is Part Of: Concurrency and computation. Volume 33:Number 13(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-28
• Subjects: efficiency -- inverse Gaussian regression -- KL estimator -- Liu estimator -- MLE -- ridge; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6222
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Multicollinearity poses an undesirable effect on the efficiency of the maximum likelihood estimator (MLE) in both Gaussian and non‐Gaussian regression models. The ridge and the Liu estimators have been developed as an alternative to the MLE. Both estimators possess smaller mean squared error (MSE) over the MLE. Recently, Kibria and Lukman developed KL estimator, which was found to outperform the ridge and the Liu estimators in the linear regression model. With this expectation, we developed the KL estimator for the inverse Gaussian regression model. We compare the proposed estimator's performance with some existing estimators in terms of theoretical comparison, the simulation study, and real‐life application. Smaller MSE criterion shows that the proposed estimator with one of its shrinkage parameter performs the best.