The importance of scale for spatial-confounding bias and precision of spatial regression estimators.
The importance of scale for spatial-confounding bias and precision of spatial regression estimators.
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DOI：
10.1214/10-sts326
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发表时间：
2010-02
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影响因子：
--
通讯作者：
Paciorek CJ
中科院分区：
文献类型：
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作者：
Paciorek CJ
回归模型中的残差通常是空间相关的。突出的例子包括为了解污染物对健康的慢性影响而进行的环境流行病学研究。我认为残差空间结构的影响的偏差和精度的回归系数，开发一个简单的框架，在其中理解的关键问题，并获得翔实的分析结果。当未测量的混杂因素将空间结构引入残差时，具有空间随机效应的回归模型和密切相关的模型（如克里金法和惩罚样条）会出现偏差，即使残差方差分量已知。分析和模拟结果表明，偏差如何取决于协变量和残差的空间尺度：只有当协变量的变化小于未测量混杂的尺度时，才能通过拟合空间模型来减少偏差。我还讨论了如何规模的残差和协变量影响效率和不确定性估计时，残差是独立的协变量。在黑碳颗粒物空气污染和出生体重之间的关联的应用中，控制大规模的空间变化似乎减少了来自未测量的混杂因素的偏差，同时增加了估计的污染影响的不确定性。
Residuals in regression models are often spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When unmeasured confounding introduces spatial structure into the residuals, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual: one can reduce bias by fitting a spatial model only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals are independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.