Bayesian hurdle discrete Weibull regression model with mixed effects

This paper proposed a Bayesian hurdle discrete Weibull regression model for clustered count data.Modeling based on discrete Weibull model can handle various dispersion type which is an attractive feature.Also in the presence of many zeros we focus on hurdle model instead of zero inflated model which is also widely used for excess zero count data.We use a Bayesian mixed effect approach for analyzing such data.MCMC Gibbs sampling algorithm was used in order to obtain the estimators within a Bayesian mixed regression model.Furthermore, the proposed model is applied to analyze the 2020 Korean medical panel (KMP) annual data to assess the factors influencing the number of emergency services used per year in 17 different regions.We compared the results with our proposed model with those of hurdle Poisson and hurdle negative binomial regression models.The result shows that using hurdle discrete Weibull regression model gives the best fit.We also held a simulation study to investigate the performance and the robustness of hurdle discrete Weibull regression model.It was shown that our proposed method gives accurate results and reveals that it can flexibly model clustered count data with various dispersion and data with many zeros.