Imputation strategy with media using regression trees

Marquez Perez, Victor Ernesto; Useche Castro, Lelly María; Mesa Avila, Dulce María; Chacon Contreras, Ana Ides

dc.creator	Marquez Perez, Victor Ernesto	es
dc.creator	Useche Castro, Lelly María	es
dc.creator	Mesa Avila, Dulce María	es
dc.creator	Chacon Contreras, Ana Ides	es
dc.date	2017-05-16
dc.date.accessioned	2025-02-04T20:40:18Z
dc.date.available	2025-02-04T20:40:18Z
dc.identifier	https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524
dc.identifier	10.15332/s2027-3355.2017.0001.01
dc.identifier.uri	http://test.repositoriodigital.com:8080/handle/123456789/86230
dc.description	An imputation design is presented to combine classication and imputation in order to improve the quality of imputed datum. Imputation is done with completely randomized missing quantitative data and using regression trees. Media imputation techniques is compared, theoretical and empirically, using regression trees, in order to develop an integral classication and imputation strategy.Unbiased estimators were obtained developing the expected value of the estimator. Estimators proprieties were evaluated trough their variance and bias development, which showed non bias. as for the unbiased estimator variance of the media, suficiency was not proved for the media estimator.	en
dc.description	An imputation design is presented to combine classification and imputation in order to improve the quality of imputed datum. Imputation is done with completely randomized missing quantitative data and using regression trees. Media imputation techniques is compared, theoretical and empirically, using regression trees, in order to develop an integral classification and imputation strategy.Unbiased estimators were obtained developing the expected value of the estimator. Estimator’s proprieties were evaluated trough their variance and bias development, which showed non bias. as for the unbiased estimator variance of the media, sufficiency was not proved for the media estimator.	es
dc.format	application/pdf
dc.format	application/octet-stream
dc.language	spa
dc.publisher	Universidad Santo Tomás	es
dc.relation	https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524/3533
dc.relation	https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524/3567
dc.source	Comunicaciones en Estadística; Vol. 10 No. 1 (2017); 9-40	en
dc.source	Comunicaciones en Estadística; Vol. 10 Núm. 1 (2017); 9-40	es
dc.source	2339-3076
dc.source	2027-3355
dc.subject	Missing data	en
dc.subject	imputation	en
dc.subject	CART	en
dc.subject	regression trees	en
dc.subject	unbiased estimators	en
dc.subject	simulation.	en
dc.subject	missing data	es
dc.subject	imputation	es
dc.subject	CART	es
dc.subject	regression trees	es
dc.subject	unbiased estimators	es
dc.subject	simulation	es
dc.title	Imputation strategy with media using regression trees	en
dc.title	Imputation strategy with media using regression trees	es
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion

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