Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)

Detalhes bibliográficos
Autor(a) principal: Guedes, Terezinha Aparecida
Data de Publicação: 2014
Outros Autores: Rossi, Robson Marcelo, Martins, Ana Beatriz Tozzo, Janeiro, Vanderly, Carneiro, José Walter Pedroza
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Acta scientiarum. Technology (Online)
DOI: 10.4025/actascitechnol.v36i4.21192
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/21192
Resumo: The objective of this analysis was to fit germination data of Rhipsalis cereuscula Haw seeds to the Weibull model with three parameters using Frequentist and Bayesian methods. Five parameterizations were compared using the Bayesian analysis to fit a prior distribution. The parameter estimates from the Frequentist method were similar to the Bayesian responses considering the following non-informative a priori distribution for the parameter vectors: gamma (10³, 10³) in the model M1, normal (0, 106) in the model M2, uniform (0, Lsup) in the model M3, exp (μ) in the model M4 and Lnormal (μ, 106) in the model M5. However, to achieve the convergence in the models M4 and M5, we applied the μ from the estimates of the Frequentist approach. The best models fitted by the Bayesian method were the M1 and M3. The adequacy of these models was based on the advantages over the Frequentist method such as the reduced computational efforts and the possibility of comparison. 
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spelling Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)Bayesian inferencegrowth curvemodelingProbabilidade e Estatística AplicadasThe objective of this analysis was to fit germination data of Rhipsalis cereuscula Haw seeds to the Weibull model with three parameters using Frequentist and Bayesian methods. Five parameterizations were compared using the Bayesian analysis to fit a prior distribution. The parameter estimates from the Frequentist method were similar to the Bayesian responses considering the following non-informative a priori distribution for the parameter vectors: gamma (10³, 10³) in the model M1, normal (0, 106) in the model M2, uniform (0, Lsup) in the model M3, exp (μ) in the model M4 and Lnormal (μ, 106) in the model M5. However, to achieve the convergence in the models M4 and M5, we applied the μ from the estimates of the Frequentist approach. The best models fitted by the Bayesian method were the M1 and M3. The adequacy of these models was based on the advantages over the Frequentist method such as the reduced computational efforts and the possibility of comparison. Universidade Estadual De Maringá2014-09-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionanálise estatísticaapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2119210.4025/actascitechnol.v36i4.21192Acta Scientiarum. Technology; Vol 36 No 4 (2014); 651-656Acta Scientiarum. Technology; v. 36 n. 4 (2014); 651-6561806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/21192/pdf_37Guedes, Terezinha AparecidaRossi, Robson MarceloMartins, Ana Beatriz TozzoJaneiro, VanderlyCarneiro, José Walter Pedrozainfo:eu-repo/semantics/openAccess2014-10-29T09:50:03Zoai:periodicos.uem.br/ojs:article/21192Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2014-10-29T09:50:03Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
title Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
spellingShingle Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
Guedes, Terezinha Aparecida
Bayesian inference
growth curve
modeling
Probabilidade e Estatística Aplicadas
Guedes, Terezinha Aparecida
Bayesian inference
growth curve
modeling
Probabilidade e Estatística Aplicadas
title_short Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
title_full Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
title_fullStr Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
title_full_unstemmed Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
title_sort Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)
author Guedes, Terezinha Aparecida
author_facet Guedes, Terezinha Aparecida
Guedes, Terezinha Aparecida
Rossi, Robson Marcelo
Martins, Ana Beatriz Tozzo
Janeiro, Vanderly
Carneiro, José Walter Pedroza
Rossi, Robson Marcelo
Martins, Ana Beatriz Tozzo
Janeiro, Vanderly
Carneiro, José Walter Pedroza
author_role author
author2 Rossi, Robson Marcelo
Martins, Ana Beatriz Tozzo
Janeiro, Vanderly
Carneiro, José Walter Pedroza
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Guedes, Terezinha Aparecida
Rossi, Robson Marcelo
Martins, Ana Beatriz Tozzo
Janeiro, Vanderly
Carneiro, José Walter Pedroza
dc.subject.por.fl_str_mv Bayesian inference
growth curve
modeling
Probabilidade e Estatística Aplicadas
topic Bayesian inference
growth curve
modeling
Probabilidade e Estatística Aplicadas
description The objective of this analysis was to fit germination data of Rhipsalis cereuscula Haw seeds to the Weibull model with three parameters using Frequentist and Bayesian methods. Five parameterizations were compared using the Bayesian analysis to fit a prior distribution. The parameter estimates from the Frequentist method were similar to the Bayesian responses considering the following non-informative a priori distribution for the parameter vectors: gamma (10³, 10³) in the model M1, normal (0, 106) in the model M2, uniform (0, Lsup) in the model M3, exp (μ) in the model M4 and Lnormal (μ, 106) in the model M5. However, to achieve the convergence in the models M4 and M5, we applied the μ from the estimates of the Frequentist approach. The best models fitted by the Bayesian method were the M1 and M3. The adequacy of these models was based on the advantages over the Frequentist method such as the reduced computational efforts and the possibility of comparison. 
publishDate 2014
dc.date.none.fl_str_mv 2014-09-12
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
análise estatística
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/21192
10.4025/actascitechnol.v36i4.21192
url http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/21192
identifier_str_mv 10.4025/actascitechnol.v36i4.21192
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/21192/pdf_37
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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. Technology; Vol 36 No 4 (2014); 651-656
Acta Scientiarum. Technology; v. 36 n. 4 (2014); 651-656
1806-2563
1807-8664
reponame:Acta scientiarum. Technology (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. Technology (Online)
collection Acta scientiarum. Technology (Online)
repository.name.fl_str_mv Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv ||actatech@uem.br
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dc.identifier.doi.none.fl_str_mv 10.4025/actascitechnol.v36i4.21192