Open AccessBayesian Multilevel Models for Count Data
Author(s) -
Olanrewaju Seun Adesina
Publication year - 2021
Publication title -
journal of the nigerian society of physical sciences
Language(s) - English
Resource type - Journals
ISSN - 2714-4704
DOI - 10.46481/jnsps.2021.168
Subject(s) - count data , weibull distribution , poisson distribution , statistics , bayesian probability , data set , overdispersion , generalized pareto distribution , mathematics , poisson regression , bayesian inference , computer science , data mining , extreme value theory , population , demography , sociology
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.