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Estimation by kernel weighting of parameters related to employment in the confinement period

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Estimation by kernel weighting of parameters related to employment in the confinement period

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dc.contributor.author Cobo, Beatriz es_ES
dc.contributor.author Castro, Luis es_ES
dc.contributor.author Rueda, Jorge es_ES
dc.date.accessioned 2024-01-11T07:55:55Z
dc.date.available 2024-01-11T07:55:55Z
dc.date.issued 2023-09-22
dc.identifier.isbn 9788413960869
dc.identifier.uri http://hdl.handle.net/10251/201756
dc.description.abstract [EN] During the period of COVID-19's confinement, new working methods that were not normally used began to be relevant. This is the case of teleworking or the use of new techniques for conducting surveys. The gold standard for carrying out surveys is probability sampling based on face-to-face interviews, but due to this situation of social isolation, non-probabilistic methods, such as online or web surveys, began to be used. However, in order to make reliable estimates from non-probability samples we must use special techniques to reduce the bias that appears in them. In this paper we will study a technique for bias reduction in non-probabilistic surveys that stands out for its promising results, known as Kernel Weighting. It requires a probabilistic sample as auxiliary information, and its performance can be improved using Machine Learning techniques, such as regularised logistic regression. We will use a non-probabilistic survey focused on studying the employment situation of the Spanish population during COVID-19, and as probabilistic survey the CIS Barometer of May 2020. We will compare the new estimates with those obtained in the original survey, observing important differences. es_ES
dc.format.extent 9 es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Regularized logistic regression es_ES
dc.subject Kernel weighting es_ES
dc.subject Employment es_ES
dc.subject Confinement period es_ES
dc.subject COVID-19 es_ES
dc.title Estimation by kernel weighting of parameters related to employment in the confinement period es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/CARMA2023.2023.16415
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Cobo, B.; Castro, L.; Rueda, J. (2023). Estimation by kernel weighting of parameters related to employment in the confinement period. Editorial Universitat Politècnica de València. 297-305. https://doi.org/10.4995/CARMA2023.2023.16415 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename CARMA 2023 - 5th International Conference on Advanced Research Methods and Analytics es_ES
dc.relation.conferencedate Junio 28-30, 2023 es_ES
dc.relation.conferenceplace Sevilla, España es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/CARMA/CARMA2023/paper/view/16415 es_ES
dc.description.upvformatpinicio 297 es_ES
dc.description.upvformatpfin 305 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\16415 es_ES


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