Carroll R, Lawson A B, Faes C, Kirby R S, Aregay M, Watjou K
Department of Public Health, Medical University of South Carolina.
Interuniversity Institute for Statistics and Statistical Bioinformatics, Hasselt University.
Environmetrics. 2016 Dec;27(8):466-478. doi: 10.1002/env.2410. Epub 2016 Sep 28.
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.
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，以便对研究区域的特定区域以及研究时间线中的特定年份进行模型选择。在此，我们通过大规模模拟研究并结合案例研究来检验这种方法的有效性。我们的结果表明，模型选择方法的一个特殊情况，即一个允许权重参数指示适当的线性预测变量是空间的、时空的还是两者混合的混合模型，为拟合这些时空模型提供了最佳选择。此外，案例研究通过轻松纳入生活方式、社会经济和物理环境变量以选择主要的时空线性预测变量，说明了这种混合模型在模型选择设置中的有效性。
