Mitigating spatial confounding by explicitly correlating Gaussian random fields
Mitigating spatial confounding by explicitly correlating Gaussian random fields
复制标题
通过显式关联高斯随机场来减轻空间混杂
作者：
Isa Marques;T. Kneib;N. Klein
空间模型被用于各种研究领域，如环境科学、流行病学或物理学。这种空间回归模型中的一个常见现象是空间混淆。当模拟响应平均值的空间索引协变量与例如作为未观察到的空间混杂因素的代理而包括在模型中的空间随机效应相关时，观察到该现象。因此，对协变量回归系数的估计可能会出现严重的偏差，对这些估计的解释不再有效。最近的文献表明，减少空间混淆的典型解决方案可能会导致误导性和违反直觉的结果。在这篇文章中，我们开发了一个计算高效的空间模型，它显式地将感兴趣的协变量的高斯随机场与主模型方程中的高斯随机场相关联，并集成了新的先验结构来减少空间混杂。从单变量的情形出发，我们将我们的先验结构扩展到多个空间混杂协变量的情形。在仿真研究中，我们的新模型灵活地检测和减少了空间数据集中的空间混淆，并且比通常使用的方法(如受限空间回归)性能更好。这些结果对任何希望解释空间回归模型中的协变量效应的应用研究人员来说都是有希望的。作为实际资料的说明，我们研究了海拔和温度对德国月平均降水量的影响。
Spatial models are used in a variety of research areas, such as environmental sciences, epidemiology, or physics. A common phenomenon in such spatial regression models is spatial confounding. This phenomenon is observed when spatially indexed covariates modeling the mean of the response are correlated with a spatial random effect included in the model, for example, as a proxy of unobserved spatial confounders. As a result, estimates for regression coefficients of the covariates can be severely biased and interpretation of these is no longer valid. Recent literature has shown that typical solutions for reducing spatial confounding can lead to misleading and counterintuitive results. In this article, we develop a computationally efficient spatial model that explicitly correlates a Gaussian random field for the covariate of interest with the Gaussian random field in the main model equation and integrates novel prior structures to reduce spatial confounding. Starting from the univariate case, we extend our prior structure also to the case of multiple spatially confounded covariates. In simulation studies, we show that our novel model flexibly detects and reduces spatial confounding in spatial datasets, and it performs better than typically used methods such as restricted spatial regression. These results are promising for any applied researcher who wishes to interpret covariate effects in spatial regression models. As a real data illustration, we study the effect of elevation and temperature on the mean of monthly precipitation in Germany.
空间混杂的光谱调整。
影响因子：
2.7
作者：
Guan,Yawen;Page,GarrittL;Reich,BrianJ;Ventrucci,Massimo;Yang,Shu
通讯作者：
Yang,Shu
DOI：
10.1214/10-sts326
发表时间：
2010-02
期刊：
Statistical science : a review journal of the Institute of Mathematical Statistics
影响因子：
--
作者：
Paciorek CJ
通讯作者：
Paciorek CJ