A Simple Computational Method for Estimating Mean Squared Prediction Error in General Small-Area Model

dc.contributor.authorChatterjee, Snigdhansu
dc.contributor.authorLahiri, P.
dc.date.accessioned2026-03-05T19:35:52Z
dc.date.issued2007
dc.descriptionSurvey Research Methods Section, American Statistical Association, 2007
dc.description.abstractThe basic requirements of second-order unbiasedness and non-negativity of the mean squared prediction error (MSPE) of an empirical best predictor (EBP) have led to different complex analytical adjustments to the naive parametric bootstrap technique for small area estimation. In this paper, we show a way to recover the basic simplicity in the parametric bootstrap method, i.e. replacement of laborious analytical calculations by computer-oriented simple techniques, without sacrificing the basic requirements in an MSPE estimator. The method works for a general class of mixed models and different techniques of parameter estimation.
dc.description.urihttp://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000812.pdf
dc.format.extent8 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2vuj9-o37g
dc.identifier.citationChatterjee, Snigdhansu, and P. Lahiri. “A Simple Computational Method for Estimating Mean Squared Prediction Error in General Small-Area Model.” Proceedings of the Section on Survey Research Methods, 2007, 3486–93.
dc.identifier.urihttp://hdl.handle.net/11603/42035
dc.language.isoen
dc.publisherASA
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.titleA Simple Computational Method for Estimating Mean Squared Prediction Error in General Small-Area Model
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-7986-0470

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