文章基本信息

标题：Alleviating Spatial Confounding for Areal Data Problems by Displacing the Geographical Centroids
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作者：Marcos Oliveira Prates ; Renato Martins Assunção ; Erica Castilho Rodrigues 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2019
卷号：14
期号：2
页码：623-647
DOI：10.1214/18-BA1123
语种：English
出版社：International Society for Bayesian Analysis
摘要：Spatial confounding between the spatial random effects and fixed effects covariates has been recently discovered and showed that it may bring misleading interpretation to the model results. Techniques to alleviate this problem are based on decomposing the spatial random effect and fitting a restricted spatial regression. In this paper, we propose a different approach: a transformation of the geographic space to ensure that the unobserved spatial random effect added to the regression is orthogonal to the fixed effects covariates. Our approach, named SPOCK, has the additional benefit of providing a fast and simple computational method to estimate the parameters. Also, it does not constrain the distribution class assumed for the spatial error term. A simulation study and real data analyses are presented to better understand the advantages of the new method in comparison with the existing ones.
关键词：Areal Data; Bayesian Statistics; Spatial Confounding; Spatial Regression.