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http://dx.doi.org/10.5351/KJAS.2018.31.2.287

A Bayesian zero-inflated Poisson regression model with random effects with application to smoking behavior  

Kim, Yeon Kyoung (Department of Applied Statistics, Chung-Ang University)
Hwang, Beom Seuk (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.2, 2018 , pp. 287-301 More about this Journal
Abstract
It is common to encounter count data with excess zeros in various research fields such as the social sciences, natural sciences, medical science or engineering. Such count data have been explained mainly by zero-inflated Poisson model and extended models. Zero-inflated count data are also often correlated or clustered, in which random effects should be taken into account in the model. Frequentist approaches have been commonly used to fit such data. However, a Bayesian approach has advantages of prior information, avoidance of asymptotic approximations and practical estimation of the functions of parameters. We consider a Bayesian zero-inflated Poisson regression model with random effects for correlated zero-inflated count data. We conducted simulation studies to check the performance of the proposed model. We also applied the proposed model to smoking behavior data from the Regional Health Survey (2015) of the Korea Centers for disease control and prevention.
Keywords
Markov chain Monte Carlo; Metropolis algorithm; random effect; smoking behavior; zero-inflated count data;
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Times Cited By KSCI : 4  (Citation Analysis)
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