Small area prediction of proportions and counts under a spatial Poisson mixed model

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UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
UDC.journalTitleStatistical Methods & Applicationses_ES
dc.contributor.authorBoubeta, Miguel
dc.contributor.authorLombardía, María José
dc.contributor.authorMorales, Domingo
dc.date.accessioned2023-12-14T10:34:01Z
dc.date.available2023-12-14T10:34:01Z
dc.date.issued2023-10-31
dc.description.abstract[Abstract]: This paper introduces an area-level Poisson mixed model with SAR(1) spatially correlated random effects. Small area predictors of proportions and counts are derived from the new model and the corresponding mean squared errors are estimated by parametric bootstrap. The behaviour of the introduced predictors is empirically investigated by running model-based simulation experiments. An application to real data from the Spanish living conditions survey of Galicia (Spain) is given. The target is the estimation of domain proportions of women under the poverty line.es_ES
dc.description.sponsorshipSupported by the Instituto Galego de Estatística, by MICINN Grants PID2020-113578RB-I00 and PGC2018-096840-B-I00, by the Generalitat Valenciana Grant PROMETEO/2021/063 and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C 2020/14), and by GAIN (Galician Innovation Agency) and the Regional Ministry of Economy, Employment and Industry Grant COV20/00604 and Centro de Investigación del Sistema Universitario de Galicia ED431G 2019/01, all of them through the ERDF.es_ES
dc.description.sponsorshipGeneralitat Valenciana; PROMETEO/2021/063es_ES
dc.description.sponsorshipXunta de Galicia; ED431C/2020/14es_ES
dc.description.sponsorshipXunta de Galicia; COV20/00604es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/2019/01es_ES
dc.identifier.citationBoubeta, M., Lombardía, M.J. & Morales, D. Small area prediction of proportions and counts under a spatial Poisson mixed model. Stat Methods Appl (2023). https://doi.org/10.1007/s10260-023-00729-7es_ES
dc.identifier.issn1613-981X
dc.identifier.urihttp://hdl.handle.net/2183/34495
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-096840-B-I00/ES/MODELOS MIXTOS Y ESTIMACION EN AREAS PEQUEÑASes_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.urihttps://doi.org/10.1007/s10260-023-00729-7es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectSmall area estimationes_ES
dc.subjectArea-level modelses_ES
dc.subjectSpatial correlationes_ES
dc.subjectCount dataes_ES
dc.subjectBootstrapes_ES
dc.subjectLiving conditions surveyes_ES
dc.subjectPoverty proportiones_ES
dc.titleSmall area prediction of proportions and counts under a spatial Poisson mixed modeles_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationc41d402d-a164-4872-82aa-46278bf7ab60
relation.isAuthorOfPublicationc0ead8a7-45d6-4532-9bf8-38b2bec77a46
relation.isAuthorOfPublication.latestForDiscoveryc41d402d-a164-4872-82aa-46278bf7ab60

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