Bootstrap for estimating the mean squared error of the spatial EBLUP

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dc.contributor.author Molina, Isabel
dc.contributor.author Salvati, Nicola
dc.contributor.author Pratesi, Monica
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2007-04-23T15:56:32Z
dc.date.available 2007-04-23T15:56:32Z
dc.date.issued 2007-04
dc.identifier.uri http://hdl.handle.net/10016/707
dc.description.abstract This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and nonparametric bootstrap procedures are proposed for estimating the mean squared error of the EBLUP (Empirical Best Linear Unbiased Predictor). A simulation study compares the bootstrap estimates with an asymptotic analytical approximation and studies the robustness to non-normality. Finally, two applications with real data are described.
dc.format.extent 619319 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 07-08
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Spatial correlation
dc.subject.other Simultaneously autoregressive process
dc.subject.other Parametric bootstrap
dc.subject.other Nonparametric bootstrap
dc.title Bootstrap for estimating the mean squared error of the spatial EBLUP
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws073408
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