Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

June 20, 2024


Pages


DOI

Article

Performance of Alternative Estimators in the Poisson-Inverse Gaussian Regression Model: Simulation and Application

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Authors

Bushra Ashraf Affiliation:
Department of Statistics, University of Sargodha, Sargodha, Pakistan.
, Muhammad Amin Affiliation:
Department of Statistics, University of Sargodha, Sargodha, Pakistan.
, Tahir Mahmood Affiliation:
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, PA12BE, Scotland, United Kingdom.
and Muhammad Faisal Affiliation:
Faculty of Health Studies, University of Bradford, Bradford, United Kingdom.


Abstract

The study proposed and compared the biased estimators for the Poisson-Inverse Gaussian regression model to deal with correlated regressors. The limitations of each biased estimator are also discussed. Additionally, some biasing parameters for the Stein estimator are proposed. The performance of estimators is evaluated with the help of a simulation study and a real-life application based on the minimum mean squared error criterion. The simulation and application findings favor the ridge estimator with specific biasing parameters because it provides less variation than others.


Keywords

Count data, Liu estimator, Maximum likelihood estimator, Multicollinearity, Poisson-Inverse Gaussian Regression, Overdispersion, Ridge estimator, Stein estimator, 62J07, 62J12


Citation

Ashraf, B., Amin, M., Mahmood, T., & Faisal, M. (2024). Performance of alternative estimators in the poisson-inverse gaussian regression model: Simulation and application. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-1493
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