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dc.contributor.authorDiz-Rosales, Naomi
dc.contributor.authorLombardía, María José
dc.contributor.authorMorales, Domingo
dc.date.accessioned2023-12-14T10:04:24Z
dc.date.available2023-12-14T10:04:24Z
dc.date.issued2023-11
dc.identifier.citationNaomi Diz-Rosales, María José Lombardía, Domingo Morales, Poverty Mapping Under Area-Level Random Regression Coefficient Poisson Models, Journal of Survey Statistics and Methodology, 2023; smad036, https://doi.org/10.1093/jssam/smad036es_ES
dc.identifier.issn2325-0992
dc.identifier.urihttp://hdl.handle.net/2183/34494
dc.description.abstract[Abstract]: Under an area-level random regression coefficient Poisson model, this article derives small area predictors of counts and proportions and introduces bootstrap estimators of the mean squared errors (MSEs). The maximum likelihood estimators of the model parameters and the mode predictors of the random effects are calculated by a Laplace approximation algorithm. Simulation experiments are implemented to investigate the behavior of the fitting algorithm, the predictors, and the MSE estimators with and without bias correction. The new statistical methodology is applied to data from the Spanish Living Conditions Survey. The target is to estimate the proportions of women and men under the poverty line by province.es_ES
dc.description.sponsorshipThis work was supported by the Ministry of Science and Innovation and the State Research Agency of the Spanish Government through the European Regional Development Fund (PID2022-136878NB-I00, PID2020-113578RB-I00 and PRE2021-100857 to Naomi Diz-Rosales funded by MCIN/AEI/10.13039/501100011033); by the Conselleria d’Innovació, Universitats, Ciéncia i Societat Digital of the Generalitat Valenciana (Prometeo/2021/063); by the Consellería de Cultura, Educación, Formación Profesional e Universidades of the Xunta de Galicia through the European Regional Development Fund (Competitive Reference Groups ED431C/2020/14, COV20/00604, and ED431G/2019/01); and by Centro de Investigación en Tecnologías de la Información y las Comunicaciones (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 centers of the Sistema Universitario de Galicia (CIGUS).es_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.language.isoenges_ES
dc.publisherOxford University Presses_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.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.relation.urihttps://doi.org/10.1093/jssam/smad036es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectBootstrapes_ES
dc.subjectPoverty proportiones_ES
dc.subjectRandom coefficientes_ES
dc.subjectPoisson regression modelses_ES
dc.subjectSmall area estimationes_ES
dc.titlePoverty Mapping Under Area-Level Random Regression Coefficient Poisson Modelses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleJournal of Survey Statistics and Methodologyes_ES


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