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:: Volume 14, Issue 2 (2-2021) ::
J. of Stat. Sci. 2021, 14(2): 307-334 Back to browse issues page
Bayesian Analysis of Spatial Count Data in Finite Populations Using Stochastic Partial Differential Equations
Negar Eghbal, Hossein Baghishani
Abstract:   (465 Views)

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.

Keywords: Spatial Beta-Binomial, Overdispersion, Approximate Bayesian Approach, Stochastic Partial Differential Equations, Car Crashes.
Full-Text [PDF 464 kb]   (23 Downloads)    
Type of Study: Research | Subject: Spatial Statistics
Received: 2020/01/30 | Accepted: 2020/06/6 | Published: 2021/02/28
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Eghbal N, Baghishani H. Bayesian Analysis of Spatial Count Data in Finite Populations Using Stochastic Partial Differential Equations. J. of Stat. Sci.. 2021; 14 (2) :307-334
URL: http://jss.irstat.ir/article-1-695-en.html


Volume 14, Issue 2 (2-2021) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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