Prediction with measurement errors in finite populations

Autores
Singer, Julio M.; Stanek III, Edward J.; Lencina, Viviana Beatriz; González, Luz Mery; Li, Wenjun; San Martino, Silvina
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors.
Fil: Singer, Julio M.. Universidade de Sao Paulo; Brasil
Fil: Stanek III, Edward J.. University of Massachussets; Estados Unidos
Fil: Lencina, Viviana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas. Instituto de Investigaciones Estadísticas; Argentina
Fil: González, Luz Mery. Universidad Nacional de Colombia; Colombia
Fil: Li, Wenjun. University of Massachussets; Estados Unidos
Fil: San Martino, Silvina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina
Materia
FINITE POPULATION
HETEROSKEDASTICITY
SUPERPOPULATION
UNBIASEDNESS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/70075
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Prediction with measurement errors in finite populationsSinger, Julio M.Stanek III, Edward J.Lencina, Viviana BeatrizGonzález, Luz MeryLi, WenjunSan Martino, SilvinaFINITE POPULATIONHETEROSKEDASTICITYSUPERPOPULATIONUNBIASEDNESShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors.Fil: Singer, Julio M.. Universidade de Sao Paulo; BrasilFil: Stanek III, Edward J.. University of Massachussets; Estados UnidosFil: Lencina, Viviana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas. Instituto de Investigaciones Estadísticas; ArgentinaFil: González, Luz Mery. Universidad Nacional de Colombia; ColombiaFil: Li, Wenjun. University of Massachussets; Estados UnidosFil: San Martino, Silvina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaElsevier Science2012-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/70075Singer, Julio M.; Stanek III, Edward J.; Lencina, Viviana Beatriz; González, Luz Mery; Li, Wenjun; et al.; Prediction with measurement errors in finite populations; Elsevier Science; Statistics & Probability Letters; 82; 2; 2-2012; 332-3390167-7152CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.spl.2011.10.013info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167715211003348info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230038/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:06:15Zoai:ri.conicet.gov.ar:11336/70075instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:06:15.795CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Prediction with measurement errors in finite populations
title Prediction with measurement errors in finite populations
spellingShingle Prediction with measurement errors in finite populations
Singer, Julio M.
FINITE POPULATION
HETEROSKEDASTICITY
SUPERPOPULATION
UNBIASEDNESS
title_short Prediction with measurement errors in finite populations
title_full Prediction with measurement errors in finite populations
title_fullStr Prediction with measurement errors in finite populations
title_full_unstemmed Prediction with measurement errors in finite populations
title_sort Prediction with measurement errors in finite populations
dc.creator.none.fl_str_mv Singer, Julio M.
Stanek III, Edward J.
Lencina, Viviana Beatriz
González, Luz Mery
Li, Wenjun
San Martino, Silvina
author Singer, Julio M.
author_facet Singer, Julio M.
Stanek III, Edward J.
Lencina, Viviana Beatriz
González, Luz Mery
Li, Wenjun
San Martino, Silvina
author_role author
author2 Stanek III, Edward J.
Lencina, Viviana Beatriz
González, Luz Mery
Li, Wenjun
San Martino, Silvina
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv FINITE POPULATION
HETEROSKEDASTICITY
SUPERPOPULATION
UNBIASEDNESS
topic FINITE POPULATION
HETEROSKEDASTICITY
SUPERPOPULATION
UNBIASEDNESS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors.
Fil: Singer, Julio M.. Universidade de Sao Paulo; Brasil
Fil: Stanek III, Edward J.. University of Massachussets; Estados Unidos
Fil: Lencina, Viviana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas. Instituto de Investigaciones Estadísticas; Argentina
Fil: González, Luz Mery. Universidad Nacional de Colombia; Colombia
Fil: Li, Wenjun. University of Massachussets; Estados Unidos
Fil: San Martino, Silvina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina
description We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors.
publishDate 2012
dc.date.none.fl_str_mv 2012-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/70075
Singer, Julio M.; Stanek III, Edward J.; Lencina, Viviana Beatriz; González, Luz Mery; Li, Wenjun; et al.; Prediction with measurement errors in finite populations; Elsevier Science; Statistics & Probability Letters; 82; 2; 2-2012; 332-339
0167-7152
CONICET Digital
CONICET
url http://hdl.handle.net/11336/70075
identifier_str_mv Singer, Julio M.; Stanek III, Edward J.; Lencina, Viviana Beatriz; González, Luz Mery; Li, Wenjun; et al.; Prediction with measurement errors in finite populations; Elsevier Science; Statistics & Probability Letters; 82; 2; 2-2012; 332-339
0167-7152
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.spl.2011.10.013
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167715211003348
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230038/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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