Dawoud–Kibria Estimator for Beta Regression Model: Simulation and Application

Abonazel, Mohamed R. and Dawoud, Issam and Awwad, Fuad A. and Lukman, Adewale F. (2022) Dawoud–Kibria Estimator for Beta Regression Model: Simulation and Application. Frontiers in Applied Mathematics and Statistics, 8. ISSN 2297-4687

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The linear regression model becomes unsuitable when the response variable is expressed as percentages, proportions, and rates. The beta regression (BR) model is more appropriate for the variable of this form. The BR model uses the conventional maximum likelihood estimator (BML), and this estimator may not be efficient when the regressors are linearly dependent. The beta ridge estimator was suggested as an alternative to BML in the literature. In this study, we developed the Dawoud–Kibria estimator to handle multicollinearity in the BR model. The properties of the new estimator are derived. We compared the performance of the estimator with the existing estimators theoretically using the mean squared error criterion. A Monte Carlo simulation and a real-life application were carried out to show the benefits of the proposed estimator. The theoretical comparison, simulation, and real-life application results revealed the superiority of the proposed estimator.

Item Type: Article
Subjects: Eurolib Press > Mathematical Science
Depositing User: Managing Editor
Date Deposited: 25 Jan 2023 05:21
Last Modified: 23 May 2024 05:36
URI: http://info.submit4journal.com/id/eprint/941

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