SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Engenharia Agrícola |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000400760 |
Resumo: | ABSTRACT: The t-Student distribution has been used to the spatial dependence modelling of soybean yield as an alternative to the normal distribution, being used for data with heavier tails or discrepant values. However, a usual Student t-distribution does not allow direct comparisons of geostatistical methods with a normal distribution. The aim of this study was to assess the soybean yield spatial variability through a reparameterized t-Student linear model, comparing the results with those of a Gaussian linear model. For parameter estimation, a complete maximum likelihood (CML) method was used through an expectation-maximization (EM) algorithm. The maps constructed with both reparameterized t-Student and normal distributions are dissimilar and present a kappa index (K) equivalent to 0.64. The reparameterized t-Student distribution is an alternative in studying data with discrepant values, showing the ability to decrease the influence of these points. |
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Engenharia Agrícola |
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SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODELEM algorithmspatial dependencegeostatisticscomplete maximum likelihoodABSTRACT: The t-Student distribution has been used to the spatial dependence modelling of soybean yield as an alternative to the normal distribution, being used for data with heavier tails or discrepant values. However, a usual Student t-distribution does not allow direct comparisons of geostatistical methods with a normal distribution. The aim of this study was to assess the soybean yield spatial variability through a reparameterized t-Student linear model, comparing the results with those of a Gaussian linear model. For parameter estimation, a complete maximum likelihood (CML) method was used through an expectation-maximization (EM) algorithm. The maps constructed with both reparameterized t-Student and normal distributions are dissimilar and present a kappa index (K) equivalent to 0.64. The reparameterized t-Student distribution is an alternative in studying data with discrepant values, showing the ability to decrease the influence of these points.Associação Brasileira de Engenharia Agrícola2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000400760Engenharia Agrícola v.37 n.4 2017reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v37n4p760-770/2017info:eu-repo/semantics/openAccessSchemmer,Rosangela C.Uribe-Opazo,Miguel A.Galea,ManuelAssumpção,Rosangela A. B.eng2017-10-17T00:00:00Zoai:scielo:S0100-69162017000400760Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2017-10-17T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false |
dc.title.none.fl_str_mv |
SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL |
title |
SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL |
spellingShingle |
SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL Schemmer,Rosangela C. EM algorithm spatial dependence geostatistics complete maximum likelihood |
title_short |
SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL |
title_full |
SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL |
title_fullStr |
SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL |
title_full_unstemmed |
SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL |
title_sort |
SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL |
author |
Schemmer,Rosangela C. |
author_facet |
Schemmer,Rosangela C. Uribe-Opazo,Miguel A. Galea,Manuel Assumpção,Rosangela A. B. |
author_role |
author |
author2 |
Uribe-Opazo,Miguel A. Galea,Manuel Assumpção,Rosangela A. B. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Schemmer,Rosangela C. Uribe-Opazo,Miguel A. Galea,Manuel Assumpção,Rosangela A. B. |
dc.subject.por.fl_str_mv |
EM algorithm spatial dependence geostatistics complete maximum likelihood |
topic |
EM algorithm spatial dependence geostatistics complete maximum likelihood |
description |
ABSTRACT: The t-Student distribution has been used to the spatial dependence modelling of soybean yield as an alternative to the normal distribution, being used for data with heavier tails or discrepant values. However, a usual Student t-distribution does not allow direct comparisons of geostatistical methods with a normal distribution. The aim of this study was to assess the soybean yield spatial variability through a reparameterized t-Student linear model, comparing the results with those of a Gaussian linear model. For parameter estimation, a complete maximum likelihood (CML) method was used through an expectation-maximization (EM) algorithm. The maps constructed with both reparameterized t-Student and normal distributions are dissimilar and present a kappa index (K) equivalent to 0.64. The reparameterized t-Student distribution is an alternative in studying data with discrepant values, showing the ability to decrease the influence of these points. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-08-01 |
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=S0100-69162017000400760 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000400760 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1809-4430-eng.agric.v37n4p760-770/2017 |
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 |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
Engenharia Agrícola v.37 n.4 2017 reponame:Engenharia Agrícola instname:Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA |
instname_str |
Associação Brasileira de Engenharia Agrícola (SBEA) |
instacron_str |
SBEA |
institution |
SBEA |
reponame_str |
Engenharia Agrícola |
collection |
Engenharia Agrícola |
repository.name.fl_str_mv |
Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA) |
repository.mail.fl_str_mv |
revistasbea@sbea.org.br||sbea@sbea.org.br |
_version_ |
1752126273280278528 |