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Distribution function estimation with calibration on principal components

dc.contributor.authorMartínez Puertas, Sergio 
dc.contributor.authorIllescas Manzano, María Dolores 
dc.contributor.authorRueda García, María del Mar 
dc.date.accessioned2023-12-19T09:35:19Z
dc.date.available2023-12-19T09:35:19Z
dc.date.issued2023-11-03
dc.identifier.issn1879-1778
dc.identifier.urihttp://hdl.handle.net/10835/14845
dc.description.abstractThe calibration method is a convenient means of incorporating auxiliary information when several parameters must be estimated. This approach has recently been used to develop new estimators for the distribution function. However, the auxiliary information available may generate a large dataset, provoking a loss of efficiency in the estimators obtained, due to over-calibration. We propose adapting the calibration using principal components, in order to avoid the negative consequences of over-calibration when estimating the distribution function.es_ES
dc.language.isoenes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleDistribution function estimation with calibration on principal componentses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.cam.2023.115189es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doihttps://doi.org/10.1016/j.cam.2023.115189


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