Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Crop Breeding and Applied Biotechnology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332016000200077 |
Resumo: | Abstract We propose a new methodology for multiple imputation when faced with missing data in multi-environmental trials with genotype-by-environment interaction, based on the imputation system developed by Krzanowski that uses the singular value decomposition (SVD) of a matrix. Several different iterative variants are described; differential weights can also be included in each variant to represent the influence of different components of SVD in the imputation process. The methods are compared through a simulation study based on three real data matrices that have values deleted randomly at different percentages, using as measure of overall accuracy a combination of the variance between imputations and their mean square deviations relative to the deleted values. The best results are shown by two of the iterative schemes that use weights belonging to the interval [0.75, 1]. These schemes provide imputations that have higher quality when compared with other multiple imputation methods based on the Krzanowski method. |
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oai:scielo:S1984-70332016000200077 |
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CBAB-1 |
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Crop Breeding and Applied Biotechnology |
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Missing value imputation in multi-environment trials: Reconsidering the Krzanowski methodSingular value decompositionweightsmissing datagenotype-by-environment interactionplant breedingAbstract We propose a new methodology for multiple imputation when faced with missing data in multi-environmental trials with genotype-by-environment interaction, based on the imputation system developed by Krzanowski that uses the singular value decomposition (SVD) of a matrix. Several different iterative variants are described; differential weights can also be included in each variant to represent the influence of different components of SVD in the imputation process. The methods are compared through a simulation study based on three real data matrices that have values deleted randomly at different percentages, using as measure of overall accuracy a combination of the variance between imputations and their mean square deviations relative to the deleted values. The best results are shown by two of the iterative schemes that use weights belonging to the interval [0.75, 1]. These schemes provide imputations that have higher quality when compared with other multiple imputation methods based on the Krzanowski method.Crop Breeding and Applied Biotechnology2016-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332016000200077Crop Breeding and Applied Biotechnology v.16 n.2 2016reponame:Crop Breeding and Applied Biotechnologyinstname:Sociedade Brasileira de Melhoramento de Plantasinstacron:CBAB10.1590/1984-70332016v16n2a13info:eu-repo/semantics/openAccessArciniegas-Alarcón,SergioGarcía-Peña,MarisolKrzanowski,Wojtekeng2016-08-01T00:00:00Zoai:scielo:S1984-70332016000200077Revistahttps://cbab.sbmp.org.br/#ONGhttps://old.scielo.br/oai/scielo-oai.phpcbabjournal@gmail.com||cbab@ufv.br1984-70331518-7853opendoar:2016-08-01T00:00Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantasfalse |
dc.title.none.fl_str_mv |
Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method |
title |
Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method |
spellingShingle |
Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method Arciniegas-Alarcón,Sergio Singular value decomposition weights missing data genotype-by-environment interaction plant breeding |
title_short |
Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method |
title_full |
Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method |
title_fullStr |
Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method |
title_full_unstemmed |
Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method |
title_sort |
Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method |
author |
Arciniegas-Alarcón,Sergio |
author_facet |
Arciniegas-Alarcón,Sergio García-Peña,Marisol Krzanowski,Wojtek |
author_role |
author |
author2 |
García-Peña,Marisol Krzanowski,Wojtek |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Arciniegas-Alarcón,Sergio García-Peña,Marisol Krzanowski,Wojtek |
dc.subject.por.fl_str_mv |
Singular value decomposition weights missing data genotype-by-environment interaction plant breeding |
topic |
Singular value decomposition weights missing data genotype-by-environment interaction plant breeding |
description |
Abstract We propose a new methodology for multiple imputation when faced with missing data in multi-environmental trials with genotype-by-environment interaction, based on the imputation system developed by Krzanowski that uses the singular value decomposition (SVD) of a matrix. Several different iterative variants are described; differential weights can also be included in each variant to represent the influence of different components of SVD in the imputation process. The methods are compared through a simulation study based on three real data matrices that have values deleted randomly at different percentages, using as measure of overall accuracy a combination of the variance between imputations and their mean square deviations relative to the deleted values. The best results are shown by two of the iterative schemes that use weights belonging to the interval [0.75, 1]. These schemes provide imputations that have higher quality when compared with other multiple imputation methods based on the Krzanowski method. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332016000200077 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332016000200077 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1984-70332016v16n2a13 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Crop Breeding and Applied Biotechnology |
publisher.none.fl_str_mv |
Crop Breeding and Applied Biotechnology |
dc.source.none.fl_str_mv |
Crop Breeding and Applied Biotechnology v.16 n.2 2016 reponame:Crop Breeding and Applied Biotechnology instname:Sociedade Brasileira de Melhoramento de Plantas instacron:CBAB |
instname_str |
Sociedade Brasileira de Melhoramento de Plantas |
instacron_str |
CBAB |
institution |
CBAB |
reponame_str |
Crop Breeding and Applied Biotechnology |
collection |
Crop Breeding and Applied Biotechnology |
repository.name.fl_str_mv |
Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantas |
repository.mail.fl_str_mv |
cbabjournal@gmail.com||cbab@ufv.br |
_version_ |
1754209187110322176 |