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Bootstrap for estimating the mean squared error of the spatial EBLUP

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorMolina, Isabel
dc.contributor.authorSalvati, Nicola
dc.contributor.authorPratesi, Monica
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaen
dc.date.accessioned2007-04-23T15:56:32Z
dc.date.available2007-04-23T15:56:32Z
dc.date.issued2007-04
dc.description.abstractThis 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.en
dc.format.extent619319 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.repecws073408
dc.identifier.urihttps://hdl.handle.net/10016/707
dc.language.isoengen
dc.language.isoengen
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometricsen
dc.relation.ispartofseries07-08en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherSpatial correlationen
dc.subject.otherSimultaneously autoregressive processen
dc.subject.otherParametric bootstrapen
dc.subject.otherNonparametric bootstrapen
dc.titleBootstrap for estimating the mean squared error of the spatial EBLUPen
dc.typeworking paper*
dspace.entity.typePublication
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