Automatic cross-validation in structured models: is it time to leave out leave-one-out?

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dc.contributor.authorAdin Urtasun, Aritz
dc.contributor.authorKrainski, Elias Teixeira
dc.contributor.authorLenzi, Amanda
dc.contributor.authorLiu, Zhedong
dc.contributor.authorMartínez-Minaya, Joaquín
dc.contributor.authorRue, Håvard
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.contributor.departmentInstitute for Advanced Materials and Mathematics - INAMAT2en
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertistate Publikoa
dc.date.accessioned2024-11-21T12:15:03Z
dc.date.available2024-11-21T12:15:03Z
dc.date.issued2024-07-01
dc.date.updated2024-11-21T12:03:54Z
dc.description.abstractStandard techniques such as leave-one-out cross-validation (LOOCV) might not be suitable for evaluating the predictive performance of models incorporating structured random effects. In such cases, the correlation between the training and test sets could have a notable impact on the model's prediction error. To overcome this issue, an automatic group construction procedure for leave-group-out cross validation (LGOCV) has recently emerged as a valuable tool for enhancing predictive performance measurement in structured models. The purpose of this paper is (i) to compare LOOCV and LGOCV within structured models, emphasizing model selection and predictive performance, and (ii) to provide real data applications in spatial statistics using complex structured models fitted with INLA, showcasing the utility of the automatic LGOCV method. First, we briefly review the key aspects of the recently proposed LGOCV method for automatic group construction in latent Gaussian models. We also demonstrate the effectiveness of this method for selecting the model with the highest predictive performance by simulating extrapolation tasks in both temporal and spatial data analyses. Finally, we provide insights into the effectiveness of the LGOCV method in modeling complex structured data, encompassing spatio-temporal multivariate count data, spatial compositional data, and spatio-temporal geospatial data.en
dc.description.sponsorshipOpen access funding provided by Universidad Pública de Navarra. This research has been supported by project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033 for Adin, A., and by project PID2020-115882RB-I00 for Martínez-Minaya, J.
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAdin, A., Krainski, E. T., Lenzi, A., Liu, Z., Martínez-Minaya, J., Rue, H. (2024) Automatic cross-validation in structured models: is it time to leave out leave-one-out?. Spatial Statistics, 62, 1-17. https://doi.org/10.1016/j.spasta.2024.100843.
dc.identifier.doi10.1016/j.spasta.2024.100843
dc.identifier.issn2211-6753
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/52556
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofSpatial Statistics (2024), vol. 62, 100843
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115882RB-I00/ES/
dc.relation.publisherversionhttps://doi.org/10.1016/j.spasta.2024.100843
dc.rights© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCross-validationen
dc.subjectHierarchical modelsen
dc.subjectINLAen
dc.subjectSpatial statisticsen
dc.titleAutomatic cross-validation in structured models: is it time to leave out leave-one-out?en
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublication6f8418c3-eae2-4388-b12a-0690e60d468f
relation.isAuthorOfPublication.latestForDiscovery6f8418c3-eae2-4388-b12a-0690e60d468f

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