Bayesian Inference for the Zero In ated Negative Binomial Regression Model Shim, Jung-Suk; Lee, Dong-Hee; Jun, Byoung-Cheol;
In this paper, we propose a Bayesian inference using the Markov Chain Monte Carlo(MCMC) method for the zero inflated negative binomial(ZINB) regression model. The proposed model allows the regression model for zero inflation probability as well as the regression model for the mean of the dependent variable. This extends the work of Jang et al. (2010) to the fully defiend ZINB regression model. In addition, we apply the proposed method to a real data example, and compare the efficiency with the zero inflated Poisson model using the DIC. Since the DIC of the ZINB is smaller than that of the ZIP, the ZINB model shows superior performance over the ZIP model in zero inflated count data with overdispersion.
Bayesian Model Selection;Latent variable;ZINB(Zero-In ated Negative Binomial);MCMC (Markov Chain Monte Carlo);
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