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Small area estimation of average compositions under multivariate nested error regression models
dc.contributor.author | Esteban, M. Dolores | |
dc.contributor.author | Lombardía, María José | |
dc.contributor.author | López Vizcaíno, María Esther | |
dc.contributor.author | Morales, Domingo | |
dc.contributor.author | Pérez, Agustín | |
dc.date.accessioned | 2023-04-18T08:16:28Z | |
dc.date.available | 2023-04-18T08:16:28Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | M.D. Esteban, M. J. Lombardía, E. López-Vizcaíno, D. Morales, A. & Pérez, "Small area estimation of average compositions under multivariate nested error regression models", Test, 2023, doi:10.1007/s11749-023-00847-0 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/32882 | |
dc.description.abstract | [Abstract]: This paper investigates the small area estimation of population averages of unit-level compositional data. The new methodology transforms the compositions into vectors of Rm and assumes that the vectors follow a multivariate nested error regression model. Empirical best predictors of domain indicators are derived from the fitted model, and their mean squared errors are estimated by parametric bootstrap. The empirical analysis of the behavior of the introduced predictors is investigated by means of simulation experiments. An application to real data from the Spanish household budget survey is given. The target is to estimate the average of proportions of annual household expenditures on food, housing and others, by Spanish provinces. | 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; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Axencia Galega de Innovación; COV20/00604 | es_ES |
dc.description.sponsorship | Supported by the Instituto Galego de Estatística, by the Grants PGC2018-096840-B-I00 and PID2020-113578RB-I00 of the Spanish Ministerio de Economía y Competitividad, by the Grant Prometeo/2021/063 of the Generalitat Valenciana, 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.language.iso | eng | es_ES |
dc.publisher | Springer Science and Business Media Deutschland GmbH | 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/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1007/s11749-023-00847-0 | es_ES |
dc.rights | Atribución 4.0 Internacional (CC BY 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Bootstrap | es_ES |
dc.subject | Compositional data | es_ES |
dc.subject | Household budget survey | es_ES |
dc.subject | Household expenditures | es_ES |
dc.subject | Multivariate nested error regression model | es_ES |
dc.subject | Small area estimation | es_ES |
dc.title | Small area estimation of average compositions under multivariate nested error regression models | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | TEST | es_ES |
dc.identifier.doi | 10.1007/s11749-023-00847-0 |
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