KIT | KIT-Bibliothek | Impressum | Datenschutz

Mitigating spatial confounding by explicitly correlating Gaussian random fields

Marques, Isa ; Kneib, Thomas; Klein, Nadja ORCID iD icon

Abstract:

Spatial models are used in a variety of research areas, such as environmental sciences, epidemiology, or physics. A common phenomenon in such spatial regression models is spatial confounding. This phenomenon is observed when spatially indexed covariates modeling the mean of the response are correlated with a spatial random effect included in the model, for example, as a proxy of unobserved spatial confounders. As a result, estimates for regression coefficients of the covariates can be severely biased and interpretation of these is no longer valid. Recent literature has shown that typical solutions for reducing spatial confounding can lead to misleading and counterintuitive results. In this article, we develop a computationally efficient spatial model that explicitly correlates a Gaussian random field for the covariate of interest with the Gaussian random field in the main model equation and integrates novel prior structures to reduce spatial confounding. Starting from the univariate case, we extend our prior structure also to the case of multiple spatially confounded covariates. In simulation studies, we show that our novel model flexibly detects and reduces spatial confounding in spatial datasets, and it performs better than typically used methods such as restricted spatial regression. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000175297
Veröffentlicht am 21.10.2024
Originalveröffentlichung
DOI: 10.1002/env.2727
Scopus
Zitationen: 11
Web of Science
Zitationen: 9
Dimensions
Zitationen: 22
Cover der Publikation
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 08.2022
Sprache Englisch
Identifikator ISSN: 1180-4009, 1099-095X
KITopen-ID: 1000175297
HGF-Programm 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups
Erschienen in Environmetrics
Verlag John Wiley and Sons
Band 33
Heft 5
Seiten Art.-Nr. e2727
Vorab online veröffentlicht am 19.04.2022
Nachgewiesen in Dimensions
Scopus
Web of Science
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page