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dc.contributor.authorRueda García, María Del Mar 
dc.contributor.authorFerri García, Ramón 
dc.contributor.authorCastro Martín, Luis 
dc.date.accessioned2021-05-05T12:39:37Z
dc.date.available2021-05-05T12:39:37Z
dc.date.issued2020-10-10
dc.identifier.citationM. Rueda, R. Ferri-García y L. Castro. 2020. «The R package NonProbEst for estimation in non-probability survey.» The R Journal (2020) 12:1 406-418.es_ES
dc.identifier.issn2073-4859
dc.identifier.urihttp://hdl.handle.net/10481/68347
dc.description.abstractDifferent inference procedures are proposed in the literature to correct selection bias that might be introduced with non-random sampling mechanisms. The R package NonProbEst enables the estimation of parameters using some of these techniques to correct selection bias in non-probability surveys. The mean and the total of the target variable are estimated using Propensity Score Adjustment, calibration, statistical matching, model-based, model-assisted and model-calibratated techniques. Confidence intervals can also obtained for each method. Machine learning algorithms can be used for estimating the propensities or for predicting the unknown values of the target variable for the non-sampled units. Variance of a given estimator is performed by two different Leave-One-Out jackknife procedures. The functionality of the package is illustrated with example data sets.es_ES
dc.language.isoenges_ES
dc.publisherTechnische Universitaet Wienes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectOfficialStatisticses_ES
dc.subjectHighPerformanceComputinges_ES
dc.subjectMachineLearninges_ES
dc.subjectMultivariatees_ES
dc.subjectSocialScienceses_ES
dc.subjectSurvivales_ES
dc.titleThe R package NonProbEst for estimation in non-probability surveyses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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