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http://dx.doi.org/10.5351/KJAS.2015.28.2.231

Zero In ated Poisson Model for Spatial Data  

Han, Junhee (Research And Statistical Support, Research Institute of Convergence for Biomedical Science and Technology, Pusan National University Yangsan Hospital)
Kim, Changhoon (Department of Preventive Medicine, Pusan National University School of Medicine)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.2, 2015 , pp. 231-239 More about this Journal
Abstract
A Poisson model is the first choice for counts data. Quasi Poisson or negative binomial models are usually used in cases of over (or under) dispersed data. However, these models might be unsuitable if the data consist of excessive number of zeros (zero inflated data). For zero inflated counts data, Zero Inflated Poisson (ZIP) or Zero Inflated Negative Binomial (ZINB) models are recommended to address the issue. In this paper, we further considered a situation where zero inflated data are spatially correlated. A mixed effect model with random effects that account for spatial autocorrelation is used to fit the data.
Keywords
Counts data; Poisson model; zero inflated data; spatial data; CAR; WinBUGS;
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