A comparative study of small area estimators

It is known that direct-survey estimators of small area parameters, calculated with the data from the given small area, often present large mean squared errors because of small sample sizes in the small areas. Model–based estimators borrow strength from other related areas to avoid this problem. How...

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Detalhes bibliográficos
Autores: Molina, Isabel, Santamaría Arana, Laureano, Morales González, Domingo
Formato: artículo
Fecha de publicación:2004
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:2099/3749
Acesso em linha:https://hdl.handle.net/2099/3749
Access Level:acceso abierto
Palavra-chave:Statistics
Inference
Mostreig (Estadística)
Inferència
Classificació AMS::62 Statistics::62D05 Sampling theory, sample surveys
Classificació AMS::62 Statistics::62F Parametric inference
Classificació AMS::62 Statistics::62J Linear inference, regression
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network_name_str España
repository_id_str
spelling A comparative study of small area estimatorsMolina, IsabelSantamaría Arana, LaureanoMorales González, DomingoStatisticsInferenceMostreig (Estadística)InferènciaClassificació AMS::62 Statistics::62D05 Sampling theory, sample surveysClassificació AMS::62 Statistics::62F Parametric inferenceClassificació AMS::62 Statistics::62J Linear inference, regressionIt is known that direct-survey estimators of small area parameters, calculated with the data from the given small area, often present large mean squared errors because of small sample sizes in the small areas. Model–based estimators borrow strength from other related areas to avoid this problem. How small should domain sample sizes be to recommend the use of model-based estimators? How robust small area estimators are with respect to the rate sample size/number of domains? To give answers or recommendations about the questions above, a Monte Carlo simulation experiment is carried out. In this simulation study, model-based estimators for small areas are compared with some standard design-based estimators. The simulation study starts with the construction of an artificial population data file, imitating a census file of an Statistical Office. A stratified random design is used to draw samples from the artificial population. Small area estimators of the mean of a continuous variable are calculated for all small areas and compared by using different performance measures. The evolution of this performance measures is studied when increasing the number of small areas, which means to decrease their sizes.Peer ReviewedInstitut d'Estadística de Catalunya20042004-01-0120072007-11-12journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2099/3749reponame: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:2099/37492026-05-27T15:37:01Z
dc.title.none.fl_str_mv A comparative study of small area estimators
title A comparative study of small area estimators
spellingShingle A comparative study of small area estimators
Molina, Isabel
Statistics
Inference
Mostreig (Estadística)
Inferència
Classificació AMS::62 Statistics::62D05 Sampling theory, sample surveys
Classificació AMS::62 Statistics::62F Parametric inference
Classificació AMS::62 Statistics::62J Linear inference, regression
title_short A comparative study of small area estimators
title_full A comparative study of small area estimators
title_fullStr A comparative study of small area estimators
title_full_unstemmed A comparative study of small area estimators
title_sort A comparative study of small area estimators
dc.creator.none.fl_str_mv Molina, Isabel
Santamaría Arana, Laureano
Morales González, Domingo
author Molina, Isabel
author_facet Molina, Isabel
Santamaría Arana, Laureano
Morales González, Domingo
author_role author
author2 Santamaría Arana, Laureano
Morales González, Domingo
author2_role author
author
dc.subject.none.fl_str_mv Statistics
Inference
Mostreig (Estadística)
Inferència
Classificació AMS::62 Statistics::62D05 Sampling theory, sample surveys
Classificació AMS::62 Statistics::62F Parametric inference
Classificació AMS::62 Statistics::62J Linear inference, regression
topic Statistics
Inference
Mostreig (Estadística)
Inferència
Classificació AMS::62 Statistics::62D05 Sampling theory, sample surveys
Classificació AMS::62 Statistics::62F Parametric inference
Classificació AMS::62 Statistics::62J Linear inference, regression
description It is known that direct-survey estimators of small area parameters, calculated with the data from the given small area, often present large mean squared errors because of small sample sizes in the small areas. Model–based estimators borrow strength from other related areas to avoid this problem. How small should domain sample sizes be to recommend the use of model-based estimators? How robust small area estimators are with respect to the rate sample size/number of domains? To give answers or recommendations about the questions above, a Monte Carlo simulation experiment is carried out. In this simulation study, model-based estimators for small areas are compared with some standard design-based estimators. The simulation study starts with the construction of an artificial population data file, imitating a census file of an Statistical Office. A stratified random design is used to draw samples from the artificial population. Small area estimators of the mean of a continuous variable are calculated for all small areas and compared by using different performance measures. The evolution of this performance measures is studied when increasing the number of small areas, which means to decrease their sizes.
publishDate 2004
dc.date.none.fl_str_mv 2004
2004-01-01
2007
2007-11-12
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/2099/3749
url https://hdl.handle.net/2099/3749
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 15,301629

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