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dc.contributor.authorSánchez-Borrego, Ismael
dc.contributor.authorRueda García, María Del Mar 
dc.contributor.authorArcos-Cebrián, Antonio
dc.identifier.citationSánchez-Borrego, I., Arcos, A. & Rueda, M. Kernel-based methods for combining information of several frame surveys. Metrika 82, 71–86 (2019).
dc.description.abstractA sample selected from a single sampling frame may not represent adequatly the entire population. Multiple frame surveys are becoming increasingly used and popular among statistical agencies and private organizations, in particular in situations where several sampling frames may provide better coverage or can reduce sampling costs for estimating population quantities of interest. Auxiliary information available at the population level is often categorical in nature, so that incorporating categorical and continuous information can improve the efficiency of the method of estimation. Nonparametric regression methods represent a widely used and flexible estimation approach in the survey context. We propose a kernel regression estimator for dual frame surveys that can handle both continuous and categorical data. This methodology is extended to multiple frame surveys. We derive theoretical properties of the proposed methods and numerical experiments indicate that the proposed estimator perform well in practical settings under different scenarios.es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividades_ES
dc.description.sponsorshipConsejería de Economía, Innovación, Ciencia y Empleoes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subjectKernel regressiones_ES
dc.subjectnonparametric regressiones_ES
dc.subjectdual frame surveyes_ES
dc.subjectmultiple frame surveyes_ES
dc.subjectmodel-assisted estimationes_ES
dc.titleKernel-based methods for combining information of several frame surveyses_ES

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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España