Bayesian Multilevel Models for Count Data


  • Olumide Sunday Adesina Department of Mathematical Sciences, Redeemer’s University, Nigeria


Count Data, Health, Insurance, Dispersion, Multilevel Models.


The traditional Poisson regression model for fitting count data is considered inadequate to fit over-or under-dispersed count data and new models have been developed to make up for such inadequacies inherent in the model. In this study, Bayesian Multi-level model was proposed using the No-U-Turn Sampler (NUTS) sampler to sample from the posterior distribution. A simulation was carried out for both over-and under-dispersed data from discrete Weibull distribution. Pareto k diagnostics was implemented, and the result showed that under-dispersed and over-dispersed simulated data has all its k value to be less than 0.5, which indicate that all the observations are good. Also all WAIC were the same as LOO-IC except for Poisson in the over-dispersed simulated data. Real-life data set from National Health Insurance Scheme (NHIS) was used for further analysis. Seven multi-level models were f itted and the Geometric model outperformed other model. 


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How to Cite

Adesina, O. S. (2021). Bayesian Multilevel Models for Count Data. Journal of the Nigerian Society of Physical Sciences, 3(3), 224–233.



Original Research