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Small area prediction of proportions and counts under a spatial Poisson mixed model
dc.contributor.author | Boubeta, Miguel | |
dc.contributor.author | Lombardía, María José | |
dc.contributor.author | Morales, Domingo | |
dc.date.accessioned | 2023-12-14T10:34:01Z | |
dc.date.available | 2023-12-14T10:34:01Z | |
dc.date.issued | 2023-10-31 | |
dc.identifier.citation | Boubeta, 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/s10260-023-00729-7 | es_ES |
dc.identifier.issn | 1613-981X | |
dc.identifier.uri | http://hdl.handle.net/2183/34495 | |
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.sponsorship | Supported 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.sponsorship | Generalitat Valenciana; PROMETEO/2021/063 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C/2020/14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; COV20/00604 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer Nature | es_ES |
dc.relation | info: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 PEQUEÑAS | es_ES |
dc.relation | info: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 APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1007/s10260-023-00729-7 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Small area estimation | es_ES |
dc.subject | Area-level models | es_ES |
dc.subject | Spatial correlation | es_ES |
dc.subject | Count data | es_ES |
dc.subject | Bootstrap | es_ES |
dc.subject | Living conditions survey | es_ES |
dc.subject | Poverty proportion | es_ES |
dc.title | Small area prediction of proportions and counts under a spatial Poisson mixed model | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Statistical Methods & Applications | es_ES |
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