Outliers as a Source of Overdispersion in Poisson Regression Modelling: Evidence from Simulation and Real Data

Authors

  • Sohel Rana Department of Mathematical & Physical Sciences, East West University, Dhaka-1212, Bangladesh
  • Abu Sayed Md Al Mamun Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
  • FM Arifur Rahman Department of Mathematical & Physical Sciences, East West University, Dhaka-1212, Bangladesh
  • Hanaa Elgohari Department of Statistics, Faculty of commerce, Mansoura University, Egypt

DOI:

https://doi.org/10.3329/ijss.v23i2.70105

Keywords:

Generalized regression, Outlier, Overdispersion, Poisson regression

Abstract

The Poisson regression model is a well-known technique for modelling count data. However, it is necessary to satisfy the overdispersion assumption in order to fit the Poisson regression model. Due to the overdispersion problem in the Poisson regression model, standard errors might be underestimated, which may lead to a highly misleading inference. There are several tests in the literature to check the presence of overdispersion in the Poisson model. In this study, we apply a regression-based t test to identify the overdispersion. The simulation study and real data example clearly show that the overdispersion in the Poisson model is caused by the existence of outliers.

International Journal of Statistical Sciences, Vol. 23(2), November, 2023, pp 31-37

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Published

2023-11-30

How to Cite

Rana, S. ., Al Mamun, A. S. M. ., Rahman, F. A. ., & Elgohari, H. . (2023). Outliers as a Source of Overdispersion in Poisson Regression Modelling: Evidence from Simulation and Real Data. International Journal of Statistical Sciences, 23(2), 31–37. https://doi.org/10.3329/ijss.v23i2.70105

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Section

Original Articles