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...
- 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:
- Universidad Santo Tomás
- Idioma:
- spa
- OAI Identifier:
- oai:repository.usta.edu.co:11634/6478
- 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
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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_ |
1800786417070112768 |