论文标题
评估克服空间混淆的最新方法
Evaluating recent methods to overcome spatial confounding
论文作者
论文摘要
空间混淆的概念与空间回归密切相关，尽管尚未确定一般定义。空间回归模型中空间混淆的一个普遍接受的想法是，固定效应估计值的变化可能会在模型中包括与协变量的空间相关随机效应界面时发生的。已经提出了不同的方法来减轻空间线性回归模型中的空间混淆，但是尚不清楚它们是否提供正确的固定效果估计值。在本文中，我们考虑了一些减轻空间混淆的建议，例如限制回归，空间+模型和改变高斯马尔可夫随机场。目的是确定哪个提供了固定效应的最佳估计。嫁妆死亡数据在2001年在1995 - 2001年的斯洛文尼亚的胃癌发病率数据和1975 - 1980年之间的苏格兰的嘴唇癌发病率数据进行了分析。进行了几项仿真研究，以评估在空间混杂的不同情况下方法的性能。结果反映出，空间+方法似乎提供了最接近真实值的固定效应估计值。
The concept of spatial confounding is closely connected to spatial regression, although no general definition has been established. A generally accepted idea of spatial confounding in spatial regression models is the change in fixed effects estimates that may occur when spatially correlated random effects collinear with the covariate are included in the model. Different methods have been proposed to alleviate spatial confounding in spatial linear regression models, but it is not clear if they provide correct fixed effects estimates. In this article, we consider some of those proposals to alleviate spatial confounding such as restricted regression, the spatial+ model, and transformed Gaussian Markov random fields. The objective is to determine which one provides the best estimates of the fixed effects. Dowry death data in Uttar Pradesh in 2001, stomach cancer incidence data in Slovenia in the period 1995-2001 and lip cancer incidence data in Scotland between the years 1975-1980 are analyzed. Several simulation studies are conducted to evaluate the performance of the methods in different scenarios of spatial confounding. Results reflect that the spatial+ method seems to provide fixed effects estimates closest to the true value.
