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All Journal Xplore: Journal of Statistics
Ahmad Rifai Nasution
Department of Statistics IPB University
Decision Sciences, Operations Research & Management Engineering Mathematics

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Perbandingan Kinerja Regresi Conway-Maxwell-Poisson dan Poisson-Tweedie dalam Mengatasi Overdispersi Melalui Data Simulasi Ahmad Rifai Nasution; Kusman Sadik; Akbar Rizki
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (310.954 KB) | DOI: 10.29244/xplore.v11i3.1018

Abstract

Poisson regression is a standard method to model count data. Modeling count data frequently causes overdispersion which means that Poisson regression is less precise to model it as Poisson regression has the assumption of equidispersion. Overdispersion can be overcome by using Conway-Maxwell-Poisson (COM-Poisson) and Poisson Tweedie (Poisson-Tw) regression. The best model is determined based on the lowest value of RMSE, absolute bias, variance of parameter estimator, AIC, and BIC. This research uses simulation data. The response variable of simulation data is generated to follow Generalized Poisson distribution with combinations of and The result of simulation study shows that COM-Poisson and Compound Poisson-Tw are the alternatives to model overdispersed count data, but COM-Poisson is better to overcome overdispersion with higher dispersion parameter.
Co-Authors Akbar Rizki Kusman Sadik