Improving small area estimation by combining surveys: new perspectives in regional statistics.

A national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be devise...

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Detalhes bibliográficos
Autores: Costa, Àlex, Satorra, A., Ventura, Eva
Formato: artículo
Fecha de publicación:2006
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2099/3785
Acesso em linha:https://hdl.handle.net/2099/3785
Access Level:acceso abierto
Palavra-chave:Inference
Multivariate analysis
Inferència
Anàlisi multivariable
Classificació AMS::62 Statistics::62J Linear inference, regression
Classificació AMS::62 Statistics::62H Multivariate analysis
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repository_id_str
spelling Improving small area estimation by combining surveys: new perspectives in regional statistics.Costa, ÀlexSatorra, A.Ventura, EvaInferenceMultivariate analysisInferènciaAnàlisi multivariableClassificació AMS::62 Statistics::62J Linear inference, regressionClassificació AMS::62 Statistics::62H Multivariate analysisA national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be devised. We investigate such methods and assess them by a Monte Carlo study. We explore how a complementary survey can be exploited in small area estimation. We use the context of the Spanish Labour Force Survey (EPA) and the Barometer in Spain for our study.Peer ReviewedInstitut d'Estadística de Catalunya20062006-01-0120072007-11-15journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2099/3785reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 2.5 Spainhttp://creativecommons.org/licenses/by-nc-nd/2.5/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099/37852026-05-27T15:37:01Z
dc.title.none.fl_str_mv Improving small area estimation by combining surveys: new perspectives in regional statistics.
title Improving small area estimation by combining surveys: new perspectives in regional statistics.
spellingShingle Improving small area estimation by combining surveys: new perspectives in regional statistics.
Costa, Àlex
Inference
Multivariate analysis
Inferència
Anàlisi multivariable
Classificació AMS::62 Statistics::62J Linear inference, regression
Classificació AMS::62 Statistics::62H Multivariate analysis
title_short Improving small area estimation by combining surveys: new perspectives in regional statistics.
title_full Improving small area estimation by combining surveys: new perspectives in regional statistics.
title_fullStr Improving small area estimation by combining surveys: new perspectives in regional statistics.
title_full_unstemmed Improving small area estimation by combining surveys: new perspectives in regional statistics.
title_sort Improving small area estimation by combining surveys: new perspectives in regional statistics.
dc.creator.none.fl_str_mv Costa, Àlex
Satorra, A.
Ventura, Eva
author Costa, Àlex
author_facet Costa, Àlex
Satorra, A.
Ventura, Eva
author_role author
author2 Satorra, A.
Ventura, Eva
author2_role author
author
dc.subject.none.fl_str_mv Inference
Multivariate analysis
Inferència
Anàlisi multivariable
Classificació AMS::62 Statistics::62J Linear inference, regression
Classificació AMS::62 Statistics::62H Multivariate analysis
topic Inference
Multivariate analysis
Inferència
Anàlisi multivariable
Classificació AMS::62 Statistics::62J Linear inference, regression
Classificació AMS::62 Statistics::62H Multivariate analysis
description A national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be devised. We investigate such methods and assess them by a Monte Carlo study. We explore how a complementary survey can be exploited in small area estimation. We use the context of the Spanish Labour Force Survey (EPA) and the Barometer in Spain for our study.
publishDate 2006
dc.date.none.fl_str_mv 2006
2006-01-01
2007
2007-11-15
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099/3785
url https://hdl.handle.net/2099/3785
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institut d'Estadística de Catalunya
publisher.none.fl_str_mv Institut d'Estadística de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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