Spatio-temporal analysis of small area health data often involves choosing a fixed set of predictors prior to the final model fit. In this paper, we propose a spatio-temporal approach of Bayesian model selection to implement model selection for certain areas of the study region as well as certain years in the study time line. Here, we examine the usefulness of this approach by way of a large-scale simulation study accompanied by a case study. Our results suggest that a special case of the model selection methods, a mixture model allowing a weight parameter to indicate if the appropriate linear predictor is spatial, spatio-temporal, or a mixture of the two, offers the best option to fitting these spatio-temporal models. In addition, the case study illustrates the effectiveness of this mixture model within the model selection setting by easily accommodating lifestyle, socio-economic, and physical environmental variables to select a predominantly spatio-temporal linear predictor.

中文翻译：

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疾病映射的时空贝叶斯模型选择

小区域健康数据的时空分析通常涉及在最终模型拟合之前选择一组固定的预测变量。在本文中，我们提出了一种贝叶斯模型选择的时空方法，以对研究区域的某些区域以及研究时间线上的某些年份实施模型选择。在这里，我们通过大规模模拟研究和案例研究来检验这种方法的有效性。我们的结果表明，模型选择方法的一种特殊情况，混合模型允许权重参数指示适当的线性预测器是空间、时空还是两者的混合，提供了拟合这些时空的最佳选择。时间模型。此外，