Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation [version 2; peer review: 2 approved, 1 approved with reservations]

• Published in: F1000 research Vol. 10; p. 548
• Main Authors: Aladeitan, Benedicta B., Adebimpe, Olukayode, Lukman, Adewale F., Oludoun, Olajumoke, Abiodun, Oluwakemi E.
• Format: Journal Article
• Language: English
• Published: London Faculty of 1000 Ltd 2021; F1000 Research Limited
• Subjects: Bias; Generalized linear models; Parameter estimation; Regression analysis; Simulation; Variables; KL estimator; Liu estimator; generalized regression model; Linear regression model; Ridge estimator
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.53987.2

Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the Kibria-Lukman (KL) estimator, though literature shows that the KL estimator is preferred. Therefore, this study sought to modify the KL estimator to mitigate the Poisson Regression Model with multicollinearity.
Methods: A simulation study and a real-life study were carried out and the performance of the new estimator was compared with some of the existing estimators.
Results: The simulation result showed the new estimator performed more efficiently than the MLE, Poisson Ridge Regression Estimator (PRE), Poisson Liu Estimator (PLE) and the Poisson KL (PKL) estimators. The real-life application also agreed with the simulation result.
Conclusions: In general, the new estimator performed more efficiently than the MLE, PRE, PLE and the PKL when multicollinearity was present.