On the Effects of Spatial Confounding in Hierarchical Models
On the Effects of Spatial Confounding in Hierarchical Models
复制标题
层次模型中空间混杂的影响
DOI：
10.1111/insr.12407
复制
发表时间：
2020
影响因子：
2
通讯作者：
João B. M. Pereira
中科院分区：
文献类型：
--
作者：
Widemberg S. Nobre;A. M. Schmidt;João B. M. Pereira
通常，在空间广义线性模型中，空间平滑的协变量与空间随机效应共线。这会影响回归系数的偏差和精度。这在空间统计学文献中称为空间混杂。我们讨论的情况下，多层次的空间模型，其中有多个观察集群的混杂问题。我们表明，即使在标准的多水平模型，它允许独立的（即空间上不相关）集群效应，集群水平的固定效应可能是有偏见的，这取决于结构的“真正的”生成机制的过程。我们提供了模拟研究，以调查在不同的混杂情况下，在随机截距模型中存在的固定效应的估计混杂的影响。空间混淆的一种补救措施是限制空间回归，其中空间随机效应被约束为与模型的固定效应正交。我们提出了一种方法来适应限制空间回归模型的多级数据，并说明它与人工数据分析。我们还简要介绍了随机截距和斜率模型中的混杂问题。
Usually, in spatial generalised linear models, covariates that are spatially smooth are collinear with spatial random effects. This affects the bias and precision of the regression coefficients. This is known in the spatial statistics literature as spatial confounding. We discuss the problem of confounding in the case of multilevel spatial models wherein there are multiple observations within clusters. We show that even under the standard multilevel model, which allows for independent (i.e. not spatially correlated) cluster effects, the cluster‐level fixed effects might be biased depending on the structure of the ‘true’ generating mechanism of the processes. We provide simulation studies in order to investigate the effects of confounding in the estimation of fixed effects present in random intercept models under different scenarios of confounding. One remedy to spatial confounding is restricted spatial regression wherein the spatial random effects are constrained to be orthogonal to the fixed effects of the model. We propose one way to fit a restricted spatial regression model for multilevel data and illustrate it with artificial data analyses. We also briefly describe the issue of confounding in random intercept and slope models.
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