Semi-functional partial linear regression with measurement error: an approach based on kNN estimation

UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.endPage261es_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicaciónes_ES
UDC.journalTitleTestes_ES
UDC.startPage235es_ES
UDC.volume34es_ES
dc.contributor.authorNovo Díaz, Silvia
dc.contributor.authorAneiros, Germán
dc.contributor.authorVieu, Philippe
dc.date.accessioned2025-05-16T09:48:13Z
dc.date.embargoEndDate2025-11-18es_ES
dc.date.embargoLift2025-11-18
dc.date.issued2025
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: http://dx.doi.org/10.1007/s11749-024-00957-3es_ES
dc.description.abstract[Abstract]: This paper focuses on a semi-parametric regression model in which the response variable is explained by the sum of two components. One of them is parametric (linear), the corresponding explanatory variable is measured with additive error and its dimension is finite (p). The other component models, in a nonparametric way, the effect of a functional variable (infinite dimension) on the response. kNN-based estimators are proposed for each component, and some asymptotic results are obtained. A simulation study illustrates the behaviour of such estimators for finite sample sizes, while an application to real data shows the usefulness of our proposal.es_ES
dc.description.sponsorshipThis research/work is part of the grants PID2020-113578RB-I00 and PID2023-147127OB-I00 ‘ERDF/EU’, funded by MCIN/AEI/10.13039/501100011033/. It has also been supported by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2024/14) and by CITIC as a centre accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021-27 operational programme (Ref. ED431G 2023/01). The authors thank two anonymous referees for their constructive comments and suggestions which helped to improve the quality of this paper.es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2024/14es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2023/01es_ES
dc.identifier.citationNovo, S., Aneiros, G. & Vieu, P. Semi-functional partial linear regression with measurement error: an approach based on kNN estimation. TEST 34, 235–261 (2025). https://doi.org/10.1007/s11749-024-00957-3es_ES
dc.identifier.doi10.1007/s11749-024-00957-3
dc.identifier.issn1133-0686
dc.identifier.urihttp://hdl.handle.net/2183/42010
dc.language.isoenges_ES
dc.publisherSpringer Science and Business Media Deutschland GmbHes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/MÉTODOS ESTADÍSTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORÍA Y APLICACIONESes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-147127OB-I00/ES/INFERENCIA ESTADISTICA UTILIZANDO METODOS FLEXIBLES PARA DATOS COMPLEJOS: TEORIA Y APPLICACIONESes_ES
dc.relation.urihttps://doi.org/10.1007/s11749-024-00957-3es_ES
dc.rightsCopyright © 2024, The Author(s) under exclusive licence to Sociedad de Estadística e Investigación Operativaes_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectErrors-in-variableses_ES
dc.subjectFunctional dataes_ES
dc.subjectkNN estimationes_ES
dc.subjectPartially linear modelses_ES
dc.subjectSemi-functional regressiones_ES
dc.titleSemi-functional partial linear regression with measurement error: an approach based on kNN estimationes_ES
dc.typejournal articlees_ES
dc.type.hasVersionAMes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationbed2d73f-eff0-4e47-a3fb-27f0cfce07d9
relation.isAuthorOfPublication449cae44-40ef-41ac-994a-834bd5a05b2f
relation.isAuthorOfPublication.latestForDiscoverybed2d73f-eff0-4e47-a3fb-27f0cfce07d9

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