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Scalable Bayesian modelling for smoothing disease risks in large spatial data sets using INLA
Spatial Statistics ( IF 2.3 ) Pub Date : 2021-02-04 , DOI: 10.1016/j.spasta.2021.100496
Erick Orozco-Acosta , Aritz Adin , María Dolores Ugarte

Several methods have been proposed in the spatial statistics literature to analyse big data sets in continuous domains. However, new methods for analysing high-dimensional areal data are still scarce. Here, we propose a scalable Bayesian modelling approach for smoothing mortality (or incidence) risks in high-dimensional data, that is, when the number of small areas is very large. The method is implemented in the R add-on package bigDM and it is based on the idea of “divide and conquer“. Although this proposal could possibly be implemented using any Bayesian fitting technique, we use INLA here (integrated nested Laplace approximations) as it is now a well-known technique, computationally efficient, and easy for practitioners to handle. We analyse the proposal’s empirical performance in a comprehensive simulation study that considers two model-free settings. Finally, the methodology is applied to analyse male colorectal cancer mortality in Spanish municipalities showing its benefits with regard to the standard approach in terms of goodness of fit and computational time.



中文翻译:

可扩展的贝叶斯建模,可使用INLA消除大型空间数据集中的疾病风险

在空间统计文献中已经提出了几种方法来分析连续域中的大数据集。但是,仍然缺乏用于分析高维面数据的新方法。在这里,我们提出了一种可扩展的贝叶斯建模方法,用于平滑高维数据中的死亡(或发生率)风险,即当小区域的数量非常大时。该方法在R附加程序包bigDM中实现它基于“分而治之”的思想。尽管可以使用任何贝叶斯拟合技术来实现该建议,但由于它是一种众所周知的技术,计算效率高,易于从业人员处理,因此我们在此处使用INLA(集成嵌套式Laplace逼近)。我们在考虑了两个无模型设置的综合模拟研究中分析了该建议的经验性能。最后,该方法被用于分析西班牙市政当局的男性结直肠癌死亡率,显示出其在拟合优度和计算时间方面相对于标准方法的优势。

更新日期:2021-02-10
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