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dc.contributor.authorDiz-Rosales, Naomi
dc.contributor.authorLombardía, María José
dc.contributor.authorMorales, Domingo
dc.date.accessioned2023-11-15T14:56:02Z
dc.date.available2023-11-15T14:56:02Z
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/2183/34236
dc.descriptionCursos e Congresos, C-155es_ES
dc.description.abstract[Abstract] In a complex socio-economic context, policy makers need highly disaggregated poverty indicators. In this work, we develop a methodology in small area estimation to derive predictors of poverty proportions under a random regression coefficient Poisson model, introducing bootstrap estimators of mean squared errors. Maximum likelihood estimators of model parameters and random effects mode predictors are calculated using a Laplace approximation algorithm. Simulation experiments are conducted to investigate the behaviour of the fitting algorithm, the predictors and the mean squared error estimator. The new statistical methodology is applied to data from the Spanish survey of living conditions to map poverty proportions by province and sex, developing a tool to support policy decision makinges_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2020/14es_ES
dc.description.sponsorshipThis research is part of the grant PID2020-113578RB-I00, funded by MCIN/AEI/10.13039/501100011033/. It has also been supported by the Spanish grant PID2022-136878NB-I00, the Valencian grant Prometeo/2021/063, by the Xunta de Galicia (Competitive Reference ED431C-2020/14) and by CITIC that is supported by Xunta de Galicia, collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Sistema Universitario de Galicia (CIGUS). The first author was also sponsoredby the Spanish Grant for Predoctoral Research Trainees RD 103/2019 being this work part of grant PRE2021-100857, funded by MCIN/AEI/10.13039/501100011033/ and ESF+
dc.language.isoenges_ES
dc.publisherUniversidade da Coruña, Servizo de Publicaciónses_ES
dc.relationinfo: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.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136878NB-I00/ES/ESTIMACION EN AREAS PEQUEÑAS Y MODELOS MULTIVARIANTES MIXTOSes_ES
dc.relation.urihttps://doi.org/10.17979/spudc.000024.18
dc.rightsAttribution 4.0 International (CC BY 4.0)es_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es*
dc.subjectAlgoritmos de aproximaciónes_ES
dc.subjectSimulaciónes_ES
dc.subjectPobrezaes_ES
dc.titleMapping the Poverty Proportion in Small Areas under Random Regression Coefficient Poisson Modelses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.startPage109es_ES
UDC.endPage115es_ES
UDC.conferenceTitleVI Congreso Xove TIC: impulsando el talento científico. Octubre, 2023, A Coruñaes_ES


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