Bayesian Multilevel Models for Count Data
| dc.contributor.author | Adesina, Olumide Sunday | |
| dc.date.accessioned | 2021-09-23T15:03:13Z | |
| dc.date.available | 2021-09-23T15:03:13Z | |
| dc.date.issued | 2021-08-29 | |
| dc.description.abstract | 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, a 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 indicates 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 fitted and the Geometric model outperformed other models. | en_US |
| dc.identifier.uri | http://dspace.run.edu.ng:8080/jspui/handle/123456789/256 | |
| dc.language.iso | en | en_US |
| dc.publisher | Nigerian Society of Physical Sciences | en_US |
| dc.relation.ispartofseries | 3;3 | |
| dc.subject | Count Data | en_US |
| dc.subject | Insurance | en_US |
| dc.subject | Dispersion | en_US |
| dc.subject | Multilevel Models | en_US |
| dc.title | Bayesian Multilevel Models for Count Data | en_US |
| dc.type | Article | en_US |