Open AccessBayesian Models for Zero Truncated Count Data
Author(s) -
Olanrewaju Seun Adesina,
Dawud Adebayo Agunbiade,
Pelumi E. Oguntunde,
Tolulope F. Adesina
Publication year - 2019
Publication title -
asian journal of probability and statistics
Language(s) - English
Resource type - Journals
ISSN - 2582-0230
DOI - 10.9734/ajpas/2019/v4i130105
Subject(s) - count data , poisson distribution , negative binomial distribution , poisson regression , zero inflated model , mathematics , bayesian probability , markov chain monte carlo , quasi likelihood , generalized linear model , statistics , zero (linguistics) , bayesian linear regression , bayesian inference , population , linguistics , philosophy , demography , sociology
It is important to fit count data with suitable model(s), models such as Poisson Regression, Quassi Poisson, Negative Binomial, to mention but a few have been adopted by researchers to fit zero truncated count data in the past. In recent times, dedicated models for fitting zero truncated count data have been developed, and they are considered sufficient. This study proposed Bayesian multi-level Poisson and Bayesian multi-level Geometric model, Bayesian Monte Carlo Markov Chain Generalized linear Mixed Models (MCMCglmms) of zero truncated Poisson and MCMCglmms Poisson regression model to fit health count data that is truncated at zero. Suitable model selection criteria were used to determine preferred models for fitting zero truncated data. Results obtained showed that Bayesian multi-level Poisson outperformed Bayesian multi-level Poisson Geometric model; also MCMCglmms of zero truncated Poisson outperformed MCMCglmms Poisson.