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dc.creatorUrdangarin Iztueta, Arantxaes_ES
dc.creatorGoicoa Mangado, Tomáses_ES
dc.creatorUgarte Martínez, María Doloreses_ES
dc.date.accessioned2023-02-20T08:08:48Z
dc.date.available2023-02-20T08:08:48Z
dc.date.issued2022
dc.identifier.citationUrdangarin, 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.issn1139-1138
dc.identifier.urihttps://hdl.handle.net/2454/44739
dc.description.abstractThe 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 inflateden
dc.description.sponsorshipOpen 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.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherSpringeren
dc.relation.ispartofRevista Matemática Complutense 1-28es_ES
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, visiten
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSpatial confoundingen
dc.subjectSpatial regression modelen
dc.subjectSpatialen
dc.subjectTransformed Gaussian Markov random fielden
dc.titleEvaluating recent methods to overcome spatial confoundingen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.date.updated2023-02-20T07:54:57Z
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.contributor.departmentInstitute for Advanced Materials and Mathematics - INAMAT2en
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.1007/s13163-022-00449-8
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/en
dc.relation.publisherversionhttps://doi.org/10.1007/s13163-022-00449-8
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoaes


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© 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
Except where otherwise noted, this item's license is described as © 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

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