In public health applications, spatial data collected are often recorded at different spatial scales and over different correlated variables. Spatial change of support is a key inferential problem in these applications and have become standard in univariate settings; however, it is less standard in multivariate settings. There are several existing multivariate spatial models that can be easily combined with multiscale spatial approach to analyze multivariate multiscale spatial data. In this paper, we propose three new models from such combinations for bivariate multiscale spatial data in a Bayesian context. In particular, we extend spatial random effects models, multivariate conditional autoregressive models, and ordered hierarchical models through a multiscale spatial approach. We run simulation studies for the three models and compare them in terms of prediction performance and computational efficiency. We motivate our models through an analysis of 2015 Texas annual average percentage receiving two blood tests from the Dartmouth Atlas Project.

翻译：在公共卫生应用中，常收集到不同空间尺度和不同相关变量的空间数据。空间支持变化是此类应用中的关键推断问题，在单变量情境中已较为成熟，但在多变量情境中仍不够标准化。目前存在多种可与多尺度空间方法结合分析多变量多尺度空间数据的多变量空间模型。本文在贝叶斯框架下，基于此类组合提出三种适用于双变量多尺度空间数据的新模型。具体而言，我们通过多尺度空间方法扩展了空间随机效应模型、多变量条件自回归模型及有序分层模型。通过模拟研究比较三种模型的预测性能与计算效率。我们以2015年达特茅斯地图项目中德克萨斯州年度平均接受两项血液检测的百分比分析作为模型的实际应用案例。