GARMA models, a new perspective using Bayesian methods and transformations

Detalhes bibliográficos
Autor(a) principal: Andrade, Breno Silveira de
Data de Publicação: 2016
Tipo de documento: Tese
Idioma: eng
Título da fonte: Repositório Institucional da UFSCAR
Texto Completo: https://repositorio.ufscar.br/handle/ufscar/8949
Resumo: Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average).
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spelling Andrade, Breno Silveira deAndrade Filho, Marinho Gomes dehttp://lattes.cnpq.br/4126245980112687http://lattes.cnpq.br/2060946751027537c2a7cd39-2032-4bab-b2d6-3c057eb8b03e2017-08-08T19:15:39Z2017-08-08T19:15:39Z2016-12-16ANDRADE, Breno Silveira de. GARMA models, a new perspective using Bayesian methods and transformations. 2016. Tese (Doutorado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/ufscar/8949.https://repositorio.ufscar.br/handle/ufscar/8949Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average).Modelos Autoregressivos e de médias móveis generalizados (GARMA) são uma classe de modelos que foi desenvolvida para extender os conhecidos modelos ARMA com distribuição Gaussiana para um cenário de series temporais não Gaussianas. Este trabalho apresenta os modelos GARMA aplicados a distribuições discretas, e alguns métodos de reamostragem aplicados neste contexto. É proposto neste trabalho uma abordagem Bayesiana para os modelos GARMA. O trabalho da continuidade apresentando os modelos GARMA transformados, utilizando a transformação de Box-Cox. E por último porém não menos importante uma abordagem Bayesiana para os modelos GARMA transformados.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)engUniversidade Federal de São CarlosCâmpus São CarlosPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEsUFSCarARMA transformado generalizadoARMA generalizadoAbordagem BayesianaDistribuições discretasDistribuições contínuasTransformed generalized ARMA modelBayesian approachDiscrete distributionsContinuous distributionsCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICAGARMA models, a new perspective using Bayesian methods and transformationsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisOnline6006006105a248-1b18-49f6-bbf3-c4006673f34ainfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARORIGINALTeseBSA.pdfTeseBSA.pdfapplication/pdf10322083https://repositorio.ufscar.br/bitstream/ufscar/8949/1/TeseBSA.pdf4c30c490934f23dbad9d5a1f087ef182MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81957https://repositorio.ufscar.br/bitstream/ufscar/8949/2/license.txtae0398b6f8b235e40ad82cba6c50031dMD52TEXTTeseBSA.pdf.txtTeseBSA.pdf.txtExtracted texttext/plain141510https://repositorio.ufscar.br/bitstream/ufscar/8949/3/TeseBSA.pdf.txt6595830b23fa01fc404d240b499f711dMD53THUMBNAILTeseBSA.pdf.jpgTeseBSA.pdf.jpgIM Thumbnailimage/jpeg4825https://repositorio.ufscar.br/bitstream/ufscar/8949/4/TeseBSA.pdf.jpgf535b6504cd3a2dfeeb059efc484b973MD54ufscar/89492023-09-18 18:31:25.645oai:repositorio.ufscar.br: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Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-09-18T18:31:25Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false
dc.title.eng.fl_str_mv GARMA models, a new perspective using Bayesian methods and transformations
title GARMA models, a new perspective using Bayesian methods and transformations
spellingShingle GARMA models, a new perspective using Bayesian methods and transformations
Andrade, Breno Silveira de
ARMA transformado generalizado
ARMA generalizado
Abordagem Bayesiana
Distribuições discretas
Distribuições contínuas
Transformed generalized ARMA model
Bayesian approach
Discrete distributions
Continuous distributions
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
title_short GARMA models, a new perspective using Bayesian methods and transformations
title_full GARMA models, a new perspective using Bayesian methods and transformations
title_fullStr GARMA models, a new perspective using Bayesian methods and transformations
title_full_unstemmed GARMA models, a new perspective using Bayesian methods and transformations
title_sort GARMA models, a new perspective using Bayesian methods and transformations
author Andrade, Breno Silveira de
author_facet Andrade, Breno Silveira de
author_role author
dc.contributor.authorlattes.por.fl_str_mv http://lattes.cnpq.br/2060946751027537
dc.contributor.author.fl_str_mv Andrade, Breno Silveira de
dc.contributor.advisor1.fl_str_mv Andrade Filho, Marinho Gomes de
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/4126245980112687
dc.contributor.authorID.fl_str_mv c2a7cd39-2032-4bab-b2d6-3c057eb8b03e
contributor_str_mv Andrade Filho, Marinho Gomes de
dc.subject.por.fl_str_mv ARMA transformado generalizado
ARMA generalizado
Abordagem Bayesiana
Distribuições discretas
Distribuições contínuas
topic ARMA transformado generalizado
ARMA generalizado
Abordagem Bayesiana
Distribuições discretas
Distribuições contínuas
Transformed generalized ARMA model
Bayesian approach
Discrete distributions
Continuous distributions
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
dc.subject.eng.fl_str_mv Transformed generalized ARMA model
Bayesian approach
Discrete distributions
Continuous distributions
dc.subject.cnpq.fl_str_mv CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
description Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average).
publishDate 2016
dc.date.issued.fl_str_mv 2016-12-16
dc.date.accessioned.fl_str_mv 2017-08-08T19:15:39Z
dc.date.available.fl_str_mv 2017-08-08T19:15:39Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv ANDRADE, Breno Silveira de. GARMA models, a new perspective using Bayesian methods and transformations. 2016. Tese (Doutorado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/ufscar/8949.
dc.identifier.uri.fl_str_mv https://repositorio.ufscar.br/handle/ufscar/8949
identifier_str_mv ANDRADE, Breno Silveira de. GARMA models, a new perspective using Bayesian methods and transformations. 2016. Tese (Doutorado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/ufscar/8949.
url https://repositorio.ufscar.br/handle/ufscar/8949
dc.language.iso.fl_str_mv eng
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dc.relation.confidence.fl_str_mv 600
600
dc.relation.authority.fl_str_mv 6105a248-1b18-49f6-bbf3-c4006673f34a
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Federal de São Carlos
Câmpus São Carlos
dc.publisher.program.fl_str_mv Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs
dc.publisher.initials.fl_str_mv UFSCar
publisher.none.fl_str_mv Universidade Federal de São Carlos
Câmpus São Carlos
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFSCAR
instname:Universidade Federal de São Carlos (UFSCAR)
instacron:UFSCAR
instname_str Universidade Federal de São Carlos (UFSCAR)
instacron_str UFSCAR
institution UFSCAR
reponame_str Repositório Institucional da UFSCAR
collection Repositório Institucional da UFSCAR
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