dc.creatorSinger, Julio M.
dc.creatorStanek, Edward J., III
dc.creatorLencina, Viviana B.
dc.creatorMery Gonzalez, Luz
dc.creatorLi, Wenjun
dc.creatorSan Martino, Silvina
dc.date.accessioned2013-10-24T17:28:44Z
dc.date.accessioned2018-07-04T16:02:42Z
dc.date.available2013-10-24T17:28:44Z
dc.date.available2018-07-04T16:02:42Z
dc.date.created2013-10-24T17:28:44Z
dc.date.issued2012
dc.identifierSTATISTICS & PROBABILITY LETTERS, AMSTERDAM, v. 82, n. 2, supl. 1, Part 3, pp. 332-339, FEB, 2012
dc.identifier0167-7152
dc.identifierhttp://www.producao.usp.br/handle/BDPI/35971
dc.identifier10.1016/j.spl.2011.10.013
dc.identifierhttp://dx.doi.org/10.1016/j.spl.2011.10.013
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1630864
dc.description.abstractWe 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. (C) 2011 Elsevier By. All rights reserved.
dc.languageeng
dc.publisherELSEVIER SCIENCE BV
dc.publisherAMSTERDAM
dc.relationSTATISTICS & PROBABILITY LETTERS
dc.rightsCopyright ELSEVIER SCIENCE BV
dc.rightsrestrictedAccess
dc.subjectFINITE POPULATION
dc.subjectHETEROSKEDASTICITY
dc.subjectSUPERPOPULATION
dc.subjectUNBIASEDNESS
dc.titlePrediction with measurement errors in finite populations
dc.typeArtículos de revistas


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