Nonlinear quantile regression to describe the dry matter accumulation of garlic plants
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Ciência Rural |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000100203 |
Resumo: | ABSTRACT: The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers. |
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Nonlinear quantile regression to describe the dry matter accumulation of garlic plantsquantile regressionnonlinear regressionAllium sativum L.growth ratecluster analysis.ABSTRACT: The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.Universidade Federal de Santa Maria2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000100203Ciência Rural v.50 n.1 2020reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20180385info:eu-repo/semantics/openAccessPuiatti,Guilherme AlvesCecon,Paulo RobertoNascimento,MoysésNascimento,Ana Carolina CampanaCarneiro,Antônio Policarpo SouzaSilva,Fabyano Fonseca ePuiatti,MárioCruz,Cosme Damiãoeng2020-01-31T00:00:00ZRevista |
dc.title.none.fl_str_mv |
Nonlinear quantile regression to describe the dry matter accumulation of garlic plants |
title |
Nonlinear quantile regression to describe the dry matter accumulation of garlic plants |
spellingShingle |
Nonlinear quantile regression to describe the dry matter accumulation of garlic plants Puiatti,Guilherme Alves quantile regression nonlinear regression Allium sativum L. growth rate cluster analysis. |
title_short |
Nonlinear quantile regression to describe the dry matter accumulation of garlic plants |
title_full |
Nonlinear quantile regression to describe the dry matter accumulation of garlic plants |
title_fullStr |
Nonlinear quantile regression to describe the dry matter accumulation of garlic plants |
title_full_unstemmed |
Nonlinear quantile regression to describe the dry matter accumulation of garlic plants |
title_sort |
Nonlinear quantile regression to describe the dry matter accumulation of garlic plants |
author |
Puiatti,Guilherme Alves |
author_facet |
Puiatti,Guilherme Alves Cecon,Paulo Roberto Nascimento,Moysés Nascimento,Ana Carolina Campana Carneiro,Antônio Policarpo Souza Silva,Fabyano Fonseca e Puiatti,Mário Cruz,Cosme Damião |
author_role |
author |
author2 |
Cecon,Paulo Roberto Nascimento,Moysés Nascimento,Ana Carolina Campana Carneiro,Antônio Policarpo Souza Silva,Fabyano Fonseca e Puiatti,Mário Cruz,Cosme Damião |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Puiatti,Guilherme Alves Cecon,Paulo Roberto Nascimento,Moysés Nascimento,Ana Carolina Campana Carneiro,Antônio Policarpo Souza Silva,Fabyano Fonseca e Puiatti,Mário Cruz,Cosme Damião |
dc.subject.por.fl_str_mv |
quantile regression nonlinear regression Allium sativum L. growth rate cluster analysis. |
topic |
quantile regression nonlinear regression Allium sativum L. growth rate cluster analysis. |
description |
ABSTRACT: The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 |
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=S0103-84782020000100203 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000100203 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-8478cr20180385 |
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 |
Universidade Federal de Santa Maria |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
dc.source.none.fl_str_mv |
Ciência Rural v.50 n.1 2020 reponame:Ciência Rural instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Ciência Rural |
collection |
Ciência Rural |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1749140554344562688 |