Analytic and bootstrap approximations of prediction errors under a multivariate fay-herriot model

e-Archivo Repository

Show simple item record

dc.contributor.author González Manteiga, Wenceslao
dc.contributor.author Lombardía, Maria J.
dc.contributor.author Molina, Isabel
dc.contributor.author Morales, Domingo
dc.contributor.author Santamaría, Laureano
dc.date.accessioned 2006-11-09T10:58:07Z
dc.date.available 2006-11-09T10:58:07Z
dc.date.issued 2005-09
dc.identifier.uri http://hdl.handle.net/10016/230
dc.description.abstract A Multivariate Fay-Herriot model is used to aid the prediction of small area parameters of dependent variables with sample data aggregated to area level. The empirical best linear unbiased predictor of the parameter vector is used, and an approximation of the elements of the mean cross product error matrix is obtained by an extension of the results of Prasad and Rao (1990) to the multiparameter case. Three different bootstrap approximations of those elements are introduced, and a simulation study is developed in order to compare the efficiency of all presented approximations, including a comparison under lack of normality. Further, the number of replications needed for the bootstrap procedures to get stabilized are studied.
dc.format.extent 491803 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 2005-10
dc.title Analytic and bootstrap approximations of prediction errors under a multivariate fay-herriot model
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws054910
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)


This item appears in the following Collection(s)

Show simple item record