AU - Eghbal, Negar
AU - Baghishani, Hossein
TI - Bayesian Analysis of Spatial Count Data in Finite Populations Using Stochastic Partial Differential Equations
PT - JOURNAL ARTICLE
TA - JSS
JN - JSS
VO - 14
VI - 2
IP - 2
4099 - http://jss.irstat.ir/article-1-695-en.html
4100 - http://jss.irstat.ir/article-1-695-en.pdf
SO - JSS 2
ABĀ - Geostatistical spatial count data in finite populations can be seen in many applications, such as urban management and medicine. The traditional model for analyzing these data is the spatial logit-binomial model. In the most applied situations, these data have overdispersion alongside the spatial variability. The binomial model is not the appropriate candidate to account for the overdispersion. The proper alternative is a beta-binomial model that has sufficient flexibility to account for the extra variability due to the possible overdispersion of counts. In this paper, we describe a Bayesian spatial beta-binomial for geostatistical count data by using a combination of the integrated nested Laplace approximation and the stochastic partial differential equations methods. We apply the methodology for analyzing the number of people injured/killed in car crashes in Mashhad, Iran. We further evaluate the performance of the model using a simulation study.
CP - IRAN
IN - Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
LG - eng
PB - JSS
PG - 307
PT - Research
YR - 2021