Locally adaptive spatial smoothing using conditional auto-regressive models
Locally adaptive spatial smoothing using conditional auto-regressive models
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DOI：
10.1111/rssc.12009
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发表时间：
2013-08-01
影响因子：
1.6
通讯作者：
Mitchell, Richard
中科院分区：
文献类型：
--
作者：
Lee, Duncan;Mitchell, Richard
作为分层贝叶斯模型的一部分，条件自回归模型通常用于捕捉面元数据中的空间对应关系。这些模型的空间相关性结构是由地理邻接决定的，但对于一些真实的数据集来说，这太简单了，它们可以直观地显示出强相关性的子区以及响应呈现阶跃变化的位置。这方面的一个例子，也是这篇论文的动机，是2005年大格拉斯哥和克莱德健康委员会271个中级地理位置的呼吸系统疾病风险的空间模式。所提出的方法是对条件自回归先验的扩展，它允许它们捕捉这种局部空间相关性并识别阶跃变化。该方法采用迭代算法的形式，除了估计剩余的参数外，还顺序地更新模型所假定的空间相关性结构。该方法的有效性通过模拟进行了评估，然后将其应用于激励大格拉斯哥的应用。
Conditional auto-regressive models are commonly used to capture spatial cor relation in areal unit data, as part of a hierarchical Bayesian model. The spatial correlation structure that is induced by these models is determined by geographical adjacency, but this is too simplistic for some real data sets, which can visually exhibit subregions of strong correlation as well as locations at which the response exhibits a step change. An example of this, and the motivation for the paper, is the spatial pattern in respiratory disease risk in the 271 intermed iate geographies in the Greater Glasgow and Clyde Health Board in 2005. The methodology proposed is an extension to the class of conditional auto-regressive priors, which allow them to capture such localized spatial correlation and to identify step changes. The approach takes the form of an iterative algorithm, which sequentially updates the spatial correlation structure that is assumed by the model in addition to estimating the remaining parameters. The efficacy of the approach is assessed by simulation, before being applied to the motivating Greater Glasgow application.