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dc.contributor.author
Singer, Julio M.
dc.contributor.author
Stanek III, Edward J.
dc.contributor.author
Lencina, Viviana Beatriz
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dc.contributor.author
González, Luz Mery
dc.contributor.author
Li, Wenjun
dc.contributor.author
San Martino, Silvina
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dc.date.available
2019-02-13T17:30:29Z
dc.date.issued
2012-02
dc.identifier.citation
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
dc.identifier.issn
0167-7152
dc.identifier.uri
http://hdl.handle.net/11336/70075
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Finite Population
dc.subject
Heteroskedasticity
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Superpopulation
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Unbiasedness
dc.subject.classification
Matemática Pura
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dc.subject.classification
Matemáticas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Prediction with measurement errors in finite populations
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-02-12T14:11:50Z
dc.journal.volume
82
dc.journal.number
2
dc.journal.pagination
332-339
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Singer, Julio M.. Universidade de Sao Paulo; Brasil
dc.description.fil
Fil: Stanek III, Edward J.. University of Massachussets; Estados Unidos
dc.description.fil
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
dc.description.fil
Fil: González, Luz Mery. Universidad Nacional de Colombia; Colombia
dc.description.fil
Fil: Li, Wenjun. University of Massachussets; Estados Unidos
dc.description.fil
Fil: San Martino, Silvina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina
dc.journal.title
Statistics & Probability Letters
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.spl.2011.10.013
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167715211003348
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230038/
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