Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification
Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification
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DOI：
10.1002/env.2331
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发表时间：
2015-06-01
期刊：
影响因子：
1.7
通讯作者：
Hoeting, Jennifer A.
中科院分区：
文献类型：
--
作者：
Hanks, Ephraim M.;Schliep, Erin M.;Hoeting, Jennifer A.
在空间广义线性混合模型（sglmm）中，空间光滑的协变量通常与空间光滑的随机效应共线。这种现象被称为空间混淆，主要在被研究过程的空间支持是离散的情况下（例如，面空间数据）进行了研究。在这种情况下，最常用的方法是受限空间回归（RSR），其中空间随机效应被约束为与固定效应正交。我们在地统计学（连续空间支持）设置中考虑空间混淆和RSR。我们发现，相对于混杂的SGLMM， RSR提供了计算优势，但在RSR下的贝叶斯可信区间可能在模型错误规范下不适当地缩小。我们提出了一种后验预测方法来缓解这一潜在问题，并讨论了RSR在各种情况下的适用性。我们通过模拟研究和分析非洲冈比亚的疟疾频率来说明RSR和SGLMM方法。版权所有：john Wiley & Sons， Ltd
In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa. Copyright (c) 2015John Wiley & Sons, Ltd.