Aladeitan BB, Adebimpe O, Lukman AF, Oludoun O, Abiodun OE. Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation.
F1000Res 2021;
10:548. [PMID:
35186265 PMCID:
PMC8825644 DOI:
10.12688/f1000research.53987.2]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/26/2021] [Indexed: 11/20/2022] Open
Abstract
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 was 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.
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