A Bayesian Analysis of Zero-Inflated Count Data: An Application to Youth Fitness Survey

Count data with excess zeroes are widely encountered in the fields of biomedical, medical, insurance and public health survey. Zero-inflated Poisson (ZIP) regression model is an useful tool to analyze such data. In this paper, a Bayesian analysis approach is proposed, where the data augmentation strategy in combination with Gibbs sampler and M-H algorithm is used to obtain the Bayesian estimates of model parameters, moreover, DIC criterion as well as Posterior Prediction P-value (ppp-value) are also considered for model selection and for assessing the goodness-of-fit of the proposed model. Finally, a real example from youth fitness survey is adapted to illustrate the application of our methodology.