论文标题
可扩展的贝叶斯建模，用于在大空间数据集中平滑疾病风险
Scalable Bayesian modeling for smoothing disease risks in large spatial data sets
论文作者
论文摘要
空间统计文献中提出了几种方法，用于分析连续域中的大数据集。但是，分析高维间数据的新方法仍然很少。在这里，我们提出了一种可扩展的贝叶斯建模方法，以平滑死亡率（或发病率）在高维数据中，即当小区域的数量很大时。该方法是在R附加软件包BIGDM中实现的。模型拟合和推理基于“分裂和征服”的概念，并使用集成的嵌套拉普拉斯近似值和数值集成。我们在考虑两个无模型设置的综合仿真研究中分析了该提案的经验性能。最后，该方法用于分析西班牙市政当局的男性结直肠癌死亡率，以表明其在适应性和计算时间的良好方面对标准方法的好处。
Several methods have been proposed in the spatial statistics literature for the analysis of big data sets in continuous domains. However, new methods for analyzing high-dimensional areal data are still scarce. Here, we propose a scalable Bayesian modeling 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. Model fitting and inference is based on the idea of "divide and conquer" and use integrated nested Laplace approximations and numerical integration. We analyze the proposal's empirical performance in a comprehensive simulation study that consider two model-free settings. Finally, the methodology is applied to analyze 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.
