Imputation strategy with media using regression trees

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...

Full description

Autores:
Marquez Perez, Victor Ernesto
Useche Castro, Lelly María
Mesa Avila, Dulce María
Chacon Contreras, Ana Ides
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad Santo Tomás
Repositorio:
Repositorio Institucional USTA
Idioma:
spa
OAI Identifier:
oai:repository.usta.edu.co:11634/6478
Acceso en línea:
https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524
Palabra clave:
missing data; imputation; CART; regression trees; unbiased estimators; simulation
Missing data; imputation; CART; regression trees; unbiased estimators; simulation.
Rights
License
Copyright (c) 2017 Comunicaciones en Estadística
id SANTTOMAS2_9c99099ffdcfcd24613c9b616235067a
oai_identifier_str oai:repository.usta.edu.co:11634/6478
network_acronym_str SANTTOMAS2
network_name_str Repositorio Institucional USTA
repository_id_str
spelling Marquez Perez, Victor ErnestoUseche Castro, Lelly MaríaMesa Avila, Dulce MaríaChacon Contreras, Ana Ides2017-05-16https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/252410.15332/s2027-3355.2017.0001.01An 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. 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. application/pdfapplication/octet-streamspaUniversidad Santo Tomáshttps://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524/3533https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524/3567Comunicaciones en Estadística; Vol. 10, Núm. 1 (2017); 9-402339-30762027-3355Comunicaciones en Estadística; Vol. 10, Núm. 1 (2017); 9-40Copyright (c) 2017 Comunicaciones en Estadísticahttp://purl.org/coar/access_right/c_abf2Imputation strategy with media using regression treesImputation strategy with media using regression treesinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1missing data; imputation; CART; regression trees; unbiased estimators; simulationMissing data; imputation; CART; regression trees; unbiased estimators; simulation.11634/6478oai:repository.usta.edu.co:11634/64782023-07-14 16:32:52.724metadata only accessRepositorio Universidad Santo Tomásnoreply@usta.edu.co
dc.title.spa.fl_str_mv Imputation strategy with media using regression trees
dc.title.alternative.eng.fl_str_mv Imputation strategy with media using regression trees
title Imputation strategy with media using regression trees
spellingShingle Imputation strategy with media using regression trees
missing data; imputation; CART; regression trees; unbiased estimators; simulation
Missing data; imputation; CART; regression trees; unbiased estimators; simulation.
title_short Imputation strategy with media using regression trees
title_full Imputation strategy with media using regression trees
title_fullStr Imputation strategy with media using regression trees
title_full_unstemmed Imputation strategy with media using regression trees
title_sort Imputation strategy with media using regression trees
dc.creator.fl_str_mv Marquez Perez, Victor Ernesto
Useche Castro, Lelly María
Mesa Avila, Dulce María
Chacon Contreras, Ana Ides
dc.contributor.author.spa.fl_str_mv Marquez Perez, Victor Ernesto
Useche Castro, Lelly María
Mesa Avila, Dulce María
Chacon Contreras, Ana Ides
dc.subject.proposal.spa.fl_str_mv missing data; imputation; CART; regression trees; unbiased estimators; simulation
topic missing data; imputation; CART; regression trees; unbiased estimators; simulation
Missing data; imputation; CART; regression trees; unbiased estimators; simulation.
dc.subject.proposal.eng.fl_str_mv Missing data; imputation; CART; regression trees; unbiased estimators; simulation.
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.
publishDate 2017
dc.date.issued.spa.fl_str_mv 2017-05-16
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.drive.none.fl_str_mv info:eu-repo/semantics/article
dc.identifier.spa.fl_str_mv https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524
10.15332/s2027-3355.2017.0001.01
url https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524
identifier_str_mv 10.15332/s2027-3355.2017.0001.01
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524/3533
https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/2524/3567
dc.relation.citationissue.spa.fl_str_mv Comunicaciones en Estadística; Vol. 10, Núm. 1 (2017); 9-40
2339-3076
2027-3355
dc.relation.citationissue.eng.fl_str_mv Comunicaciones en Estadística; Vol. 10, Núm. 1 (2017); 9-40
dc.rights.spa.fl_str_mv Copyright (c) 2017 Comunicaciones en Estadística
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Copyright (c) 2017 Comunicaciones en Estadística
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.spa.fl_str_mv application/pdf
application/octet-stream
dc.publisher.spa.fl_str_mv Universidad Santo Tomás
institution Universidad Santo Tomás
repository.name.fl_str_mv Repositorio Universidad Santo Tomás
repository.mail.fl_str_mv noreply@usta.edu.co
_version_ 1782026365900423168

Publicaciones similares