Evaluating recent methods to overcome spatial confounding

Urdangarin Iztueta, Arantxa; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores

Evaluating recent methods to overcome spatial confounding

dc.contributor.author	Urdangarin Iztueta, Arantxa
dc.contributor.author	Goicoa Mangado, Tomás
dc.contributor.author	Ugarte Martínez, María Dolores
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.contributor.department	Institute for Advanced Materials and Mathematics - INAMAT2	en
dc.contributor.funder	Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa	es
dc.date.accessioned	2023-02-20T08:08:48Z
dc.date.available	2023-02-20T08:08:48Z
dc.date.issued	2022
dc.date.updated	2023-02-20T07:54:57Z
dc.description.abstract	The concept of spatial confounding is closely connected to spatial regression, although no general definition has been established. A generally accepted idea of spatial confounding in spatial regression models is the change in fixed effects estimates that may occur when spatially correlated random effects collinear with the covariate are included in the model. Different methods have been proposed to alleviate spatial confounding in spatial linear regression models, but it is not clear if they provide correct fixed effects estimates. In this article, we consider some of those proposals to alleviate spatial confounding such as restricted regression, the spatial+ model, and transformed Gaussian Markov random fields. The objective is to determine which one provides the best estimates of the fixed effects. Dowry death data in Uttar Pradesh in 2001, stomach cancer incidence data in Slovenia in the period 1995–2001 and lip cancer incidence data in Scotland between the years 1975–1980 are analyzed. Several simulation studies are conducted to evaluate the performance of the methods in different scenarios of spatial confounding. Results reflect that the spatial+ method seems to provide fixed effects estimates closest to the true value although standard errors could be inflated	en
dc.description.sponsorship	Open Access funding provided by Universidad Pública de Navarra. This work has been supported by Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033.	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Urdangarin, A., Goicoa, T., Ugarte, M. D. (2022) Evaluating recent methods to overcome spatial confounding. Revista Matemática Complutense, 1-28. https://doi.org/10.1007/s13163-022-00449-8.	en
dc.identifier.doi	10.1007/s13163-022-00449-8
dc.identifier.issn	1139-1138
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/44739
dc.language.iso	eng	en
dc.publisher	Springer	en
dc.relation.ispartof	Revista Matemática Complutense 1-28	es_ES
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/
dc.relation.publisherversion	https://doi.org/10.1007/s13163-022-00449-8
dc.rights	© The author(s). s article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	Spatial confounding	en
dc.subject	Spatial regression model	en
dc.subject	Spatial	en
dc.subject	Transformed Gaussian Markov random field	en
dc.title	Evaluating recent methods to overcome spatial confounding	en
dc.type	info:eu-repo/semantics/article
dc.type.version	info:eu-repo/semantics/publishedVersion
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