TY - JOUR
JF - JSS
JO - JSS
VL - 14
IS - 2
PY - 2021
Y1 - 2021/2/01
TI - Bayesian Analysis of Spatial Count Data in Finite Populations Using Stochastic Partial Differential Equations
TT - تحلیل بیزی دادههای شمارشی فضایی در جوامع متناهی با رهیافت معادلات دیفرانسیل جزئی تصادفی
N2 - 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.
SP - 307
EP - 334
AU - Eghbal, Negar
AU - Baghishani, Hossein
AD - Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
KW - Spatial Beta-Binomial
KW - Overdispersion
KW - Approximate Bayesian Approach
KW - Stochastic Partial Differential Equations
KW - Car Crashes.
UR - http://jss.irstat.ir/article-1-695-en.html
DO - 10.29252/jss.14.2.14
ER -