A Bayesian zero-inflated Poisson regression model with random effects with application to smoking behavior
- Authors
- Kim, Yeon Kyoung; Hwang, Beom Seuk
- Issue Date
- Apr-2018
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- Markov chain Monte Carlo; Metropolis algorithm; random effect; smoking behavior; zero-inflated count data
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.31, no.2, pp 287 - 301
- Pages
- 15
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 31
- Number
- 2
- Start Page
- 287
- End Page
- 301
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/2306
- DOI
- 10.5351/KJAS.2018.31.2.287
- ISSN
- 1225-066X
2383-5818
- 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.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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