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A goodness-of-fit test for regression models with spatially correlated errors

dc.contributor.authorMeilán-Vila, Andrea
dc.contributor.authorOpsomer, Jean
dc.contributor.authorFrancisco-Fernández, Mario
dc.contributor.authorCrujeiras-Casais, Rosa M.
dc.date.accessioned2023-11-24T14:42:47Z
dc.date.available2023-11-24T14:42:47Z
dc.date.issued2020
dc.identifier.citationMeilán-Vila, A., Opsomer, J.D., Francisco-Fernández, M. et al. A goodness-of-fit test for regression models with spatially correlated errors. TEST 29, 728–749 (2020). https://doi.org/10.1007/s11749-019-00678-yes_ES
dc.identifier.urihttp://hdl.handle.net/2183/34328
dc.descriptionVersión final aceptada de: https://doi.org/10.1007/s11749-019-00678-yes_ES
dc.descriptionThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11749-019-00678-yes_ES
dc.description.abstractThe problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on a -distance comparing a parametric and nonparametric regression estimators is proposed. Asymptotic properties of the test statistic, both under the null hypothesis and under local alternatives, are derived. Additionally, a bootstrap procedure is designed to calibrate the test in practice. Finite sample performance of the test is analyzed through a simulation study, and its applicability is illustrated using a real data example.es_ES
dc.description.sponsorshipThe authors acknowledge the support from the Xunta de Galicia Grant ED481A-2017/361 and the European Union (European Social Fund—ESF). This research has been partially supported by MINECO Grants MTM2014-52876-R, MTM2016-76969-P and MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-015 and ED431C-2017-38, and Centro Singular de Investigación de Galicia ED431G/01), all of them through the ERDF. We also thank two reviewers and the Associate Editor for their helpful comments and suggestions that significantly improved this article.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.description.sponsorshipXunta de Galicia; ED481A 2017/361es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2016-015es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2017-38es_ES
dc.language.isoenges_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRIes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2016-76969-P/ES/MODELIZACION NO PARAMETRICA DE DINAMICAS Y DEPENDENCIAS EN SISTEMAS COMPLEJOSes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSIONes_ES
dc.relation.isversionofhttps://doi.org/10.1007/s11749-019-00678-y
dc.relation.urihttps://link.springer.com/article/10.1007/s11749-019-00678-yes_ES
dc.rightsTodos os dereitos reservados. All rights reserved.es_ES
dc.subjectModel checkinges_ES
dc.subjectSpatial correlationes_ES
dc.subjectLocal linear regressiones_ES
dc.subjectLeast squareses_ES
dc.subjectBootstrapes_ES
dc.titleA goodness-of-fit test for regression models with spatially correlated errorses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1007/s11749-019-00678-y


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