The KL estimator for the inverse Gaussian regression model

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) o...

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Bibliographic Details
Published in:Concurrency and computation 2021-07, Vol.33 (13), p.n/a
Main Authors: Lukman, Adewale F., Algamal, Zakariya Y., Kibria, B. M. Golam, Ayinde, Kayode
Format: Article
Language:English
Subjects:
efficiency
inverse Gaussian regression
KL estimator
Liu estimator
Maximum likelihood estimators
MLE
Regression models
ridge
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Summary: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.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.6222