Data variability in the imputation quality of missing data
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Agronomy (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/66185 |
Resumo: | Imputation methods were developed to define estimates for missing data and hence solve possible problems generated by the loss of this information. This study aims to assess whether data variability influences the results obtained after applying an imputation method. Incomplete databases were generated from complete real databases of experiments of tomato plants conducted using the randomized block design with three replications and 12 treatments by removing different amounts of data. The evaluated variables consisted of fruit weight per plant, number of fruits per plant, and average fruit length and width, forming eight balanced databases. Subsequently, the distribution-free multiple imputation method was applied, generating complete databases from imputation. The number of missing information influenced the accuracy measures for the data in this study. Data imputation was inadequate when there was high variability but more precise and accurate in cases of low variability. It confirmed the importance of assessing data variability before choosing to apply the imputation method. |
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Acta Scientiarum. Agronomy (Online) |
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Data variability in the imputation quality of missing dataData variability in the imputation quality of missing datamissing data; data imputation; randomized block design; distribution-free multiple imputation.missing data; data imputation; randomized block design; distribution-free multiple imputation.Imputation methods were developed to define estimates for missing data and hence solve possible problems generated by the loss of this information. This study aims to assess whether data variability influences the results obtained after applying an imputation method. Incomplete databases were generated from complete real databases of experiments of tomato plants conducted using the randomized block design with three replications and 12 treatments by removing different amounts of data. The evaluated variables consisted of fruit weight per plant, number of fruits per plant, and average fruit length and width, forming eight balanced databases. Subsequently, the distribution-free multiple imputation method was applied, generating complete databases from imputation. The number of missing information influenced the accuracy measures for the data in this study. Data imputation was inadequate when there was high variability but more precise and accurate in cases of low variability. It confirmed the importance of assessing data variability before choosing to apply the imputation method.Imputation methods were developed to define estimates for missing data and hence solve possible problems generated by the loss of this information. This study aims to assess whether data variability influences the results obtained after applying an imputation method. Incomplete databases were generated from complete real databases of experiments of tomato plants conducted using the randomized block design with three replications and 12 treatments by removing different amounts of data. The evaluated variables consisted of fruit weight per plant, number of fruits per plant, and average fruit length and width, forming eight balanced databases. Subsequently, the distribution-free multiple imputation method was applied, generating complete databases from imputation. The number of missing information influenced the accuracy measures for the data in this study. Data imputation was inadequate when there was high variability but more precise and accurate in cases of low variability. It confirmed the importance of assessing data variability before choosing to apply the imputation method.Universidade Estadual de Maringá2024-04-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/6618510.4025/actasciagron.v46i1.66185Acta Scientiarum. Agronomy; Vol 46 No 1 (2024): Publicação contínua; e66185Acta Scientiarum. Agronomy; v. 46 n. 1 (2024): Publicação contínua; e661851807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/66185/751375157356Copyright (c) 2024 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessStochero, Elisandra Lúcia Moro Dal'Col Lúcio, AlessandroJacobi, Luciane Flores 2024-05-15T12:00:43Zoai:periodicos.uem.br/ojs:article/66185Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2024-05-15T12:00:43Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Data variability in the imputation quality of missing data Data variability in the imputation quality of missing data |
title |
Data variability in the imputation quality of missing data |
spellingShingle |
Data variability in the imputation quality of missing data Stochero, Elisandra Lúcia Moro missing data; data imputation; randomized block design; distribution-free multiple imputation. missing data; data imputation; randomized block design; distribution-free multiple imputation. |
title_short |
Data variability in the imputation quality of missing data |
title_full |
Data variability in the imputation quality of missing data |
title_fullStr |
Data variability in the imputation quality of missing data |
title_full_unstemmed |
Data variability in the imputation quality of missing data |
title_sort |
Data variability in the imputation quality of missing data |
author |
Stochero, Elisandra Lúcia Moro |
author_facet |
Stochero, Elisandra Lúcia Moro Dal'Col Lúcio, Alessandro Jacobi, Luciane Flores |
author_role |
author |
author2 |
Dal'Col Lúcio, Alessandro Jacobi, Luciane Flores |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Stochero, Elisandra Lúcia Moro Dal'Col Lúcio, Alessandro Jacobi, Luciane Flores |
dc.subject.por.fl_str_mv |
missing data; data imputation; randomized block design; distribution-free multiple imputation. missing data; data imputation; randomized block design; distribution-free multiple imputation. |
topic |
missing data; data imputation; randomized block design; distribution-free multiple imputation. missing data; data imputation; randomized block design; distribution-free multiple imputation. |
description |
Imputation methods were developed to define estimates for missing data and hence solve possible problems generated by the loss of this information. This study aims to assess whether data variability influences the results obtained after applying an imputation method. Incomplete databases were generated from complete real databases of experiments of tomato plants conducted using the randomized block design with three replications and 12 treatments by removing different amounts of data. The evaluated variables consisted of fruit weight per plant, number of fruits per plant, and average fruit length and width, forming eight balanced databases. Subsequently, the distribution-free multiple imputation method was applied, generating complete databases from imputation. The number of missing information influenced the accuracy measures for the data in this study. Data imputation was inadequate when there was high variability but more precise and accurate in cases of low variability. It confirmed the importance of assessing data variability before choosing to apply the imputation method. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-04-03 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/66185 10.4025/actasciagron.v46i1.66185 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/66185 |
identifier_str_mv |
10.4025/actasciagron.v46i1.66185 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/66185/751375157356 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2024 Acta Scientiarum. Agronomy https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2024 Acta Scientiarum. Agronomy https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Agronomy; Vol 46 No 1 (2024): Publicação contínua; e66185 Acta Scientiarum. Agronomy; v. 46 n. 1 (2024): Publicação contínua; e66185 1807-8621 1679-9275 reponame:Acta Scientiarum. Agronomy (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Agronomy (Online) |
collection |
Acta Scientiarum. Agronomy (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br |
_version_ |
1799305901414285312 |