Bayesian Inference for the Zero In ated Negative Binomial Regression Model

- Journal title : Korean Journal of Applied Statistics
- Volume 24, Issue 5, 2011, pp.951-961
- Publisher : The Korean Statistical Society
- DOI : 10.5351/KJAS.2011.24.5.951

Title & Authors

Bayesian Inference for the Zero In ated Negative Binomial Regression Model

Shim, Jung-Suk; Lee, Dong-Hee; Jun, Byoung-Cheol;

Shim, Jung-Suk; Lee, Dong-Hee; Jun, Byoung-Cheol;

Abstract

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.

Keywords

Bayesian Model Selection;Latent variable;ZINB(Zero-In ated Negative Binomial);MCMC (Markov Chain Monte Carlo);

Language

Korean

Cited by

1.

허들모형에 대한 베이지안 추론,선지영;심정숙;정병철;

Journal of the Korean Data Analysis Society, 2014. vol.16. 4B, pp.1837-1847

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