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dc.contributor.authorFerri García, Ramón 
dc.contributor.authorCastro-Martín, Luis
dc.contributor.authorRueda García, María Del Mar 
dc.date.accessioned2021-05-14T07:42:35Z
dc.date.available2021-05-14T07:42:35Z
dc.date.issued2021-08
dc.identifier.citationRamón Ferri-García, Luis Castro-Martín, María del Mar Rueda, Evaluating Machine Learning methods for estimation in online surveys with superpopulation modeling, Mathematics and Computers in Simulation, Volume 186, 2021, Pages 19-28, ISSN 0378-4754, https://doi.org/10.1016/j.matcom.2020.03.005.es_ES
dc.identifier.urihttp://hdl.handle.net/10481/68515
dc.description.abstractOnline surveys, despite their cost and effort advantages, are particularly prone to selection bias due to the differences between target population and potentially covered population (online population). This leads to the unreliability of estimates coming from online samples unless further adjustments are applied. Some techniques have arisen in the last years regarding this issue, among which superpopulation modeling can be useful in Big Data context where censuses are accessible. This technique uses the sample to train a model capturing the behavior of a target variable which is to be estimated, and applies it to the nonsampled individuals to obtain population-level estimates. The modeling step has been usually done with linear regression or LASSO models, but machine learning (ML) algorithms have been pointed out as promising alternatives. In this study we examine the use of these algorithms in the online survey context, in order to evaluate and compare their performance and adequacy to the problem. A simulation study shows that ML algorithms can effectively volunteering bias to a greater extent than traditional methods in several scenarios.es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad, Spaines_ES
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades, Spaines_ES
dc.language.isoenges_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectSuperpopulation modelinges_ES
dc.subjectMachine Learninges_ES
dc.subjectOnline surveyses_ES
dc.subjectSimulationes_ES
dc.titleEvaluating Machine Learning methods for estimation in online surveys with superpopulation modelinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.projectIDMTM2015-63609-Res_ES
dc.relation.projectIDFPU17/02177es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doihttps://doi.org/10.1016/j.matcom.2020.03.005
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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