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Analysis of Space-Time Count Data Using the Flexible Gamma-Count Model
Mahsa Nadifar, Hossein Baghishani , Afshin Fallah
Abstract:   (178 Views)
Many of spatial-temporal data, particularly in medicine and disease mapping, are counts. Typically, these types of count data have extra variability that distrusts the classical Poisson modelchr('39')s performance. Therefore, incorporating this variability into the modeling process, plays an essential role in improving the efficiency of spatial-temporal data analysis. For this purpose, in this paper, a new Bayesian spatial-temporal model, called gamma count, with enough flexibility in modeling dispersion is introduced. For implementing statistical inference in the proposed model, the integrated nested Laplace approximation method is applied. A simulation study was performed to evaluate the performance of the proposed model compared to the traditional models. In addition, the application of the model has been demonstrated in analyzing leukemia data in Khorasan Razavi province, Iran.
Keywords: Spatial-Temporal Interaction, Over-Dispersion, Penalized Complexity Prior, Count Data, Gamma Count Model.
Full-Text [PDF 15091 kb]   (4 Downloads)    
Type of Study: Research | Subject: Spatial Statistics
Received: 2020/04/9 | Accepted: 2021/09/1
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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