Bootstrap methods for generalized autoregressive moving average models

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Matheus de Vasconcellos Barroso
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal de Minas Gerais
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Bootstrap (Estatística)
Estatística
Análise de séries temporais
MBB
Time- Series BOOTSTRAP
GARMA models
Link de acesso: https://hdl.handle.net/1843/BIRC-BB4NX5
Resumo: This final paper aims to find a suitable Bootstrap Method for the Generalized Autoregressive Moving Average Model. The focus is on the Moving Block Bootstrap (MBB) resampling scheme with its performance being evaluated through a Monte Carlo study and contrasted to their asymptotic Gaussian counterpart. It is stablished that the aforementioned resampling procedure can generate good estimates of parameters bias and confidence intervals. Though, the results rely heavily on the simulated model parameters and block lengths used in the MBB procedure.
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network_name_str Repositório Institucional da UFMG
repository_id_str
spelling Bootstrap methods for generalized autoregressive moving average modelsBootstrap (Estatística)EstatísticaAnálise de séries temporaisMBBTime- Series BOOTSTRAPGARMA modelsThis final paper aims to find a suitable Bootstrap Method for the Generalized Autoregressive Moving Average Model. The focus is on the Moving Block Bootstrap (MBB) resampling scheme with its performance being evaluated through a Monte Carlo study and contrasted to their asymptotic Gaussian counterpart. It is stablished that the aforementioned resampling procedure can generate good estimates of parameters bias and confidence intervals. Though, the results rely heavily on the simulated model parameters and block lengths used in the MBB procedure.Universidade Federal de Minas Gerais2019-08-11T14:51:56Z2025-09-08T23:30:16Z2019-08-11T14:51:56Z2018-06-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/1843/BIRC-BB4NX5Matheus de Vasconcellos Barrosoinfo:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMG2025-09-08T23:30:16Zoai:repositorio.ufmg.br:1843/BIRC-BB4NX5Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oairepositorio@ufmg.bropendoar:2025-09-08T23:30:16Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.none.fl_str_mv Bootstrap methods for generalized autoregressive moving average models
title Bootstrap methods for generalized autoregressive moving average models
spellingShingle Bootstrap methods for generalized autoregressive moving average models
Matheus de Vasconcellos Barroso
Bootstrap (Estatística)
Estatística
Análise de séries temporais
MBB
Time- Series BOOTSTRAP
GARMA models
title_short Bootstrap methods for generalized autoregressive moving average models
title_full Bootstrap methods for generalized autoregressive moving average models
title_fullStr Bootstrap methods for generalized autoregressive moving average models
title_full_unstemmed Bootstrap methods for generalized autoregressive moving average models
title_sort Bootstrap methods for generalized autoregressive moving average models
author Matheus de Vasconcellos Barroso
author_facet Matheus de Vasconcellos Barroso
author_role author
dc.contributor.author.fl_str_mv Matheus de Vasconcellos Barroso
dc.subject.por.fl_str_mv Bootstrap (Estatística)
Estatística
Análise de séries temporais
MBB
Time- Series BOOTSTRAP
GARMA models
topic Bootstrap (Estatística)
Estatística
Análise de séries temporais
MBB
Time- Series BOOTSTRAP
GARMA models
description This final paper aims to find a suitable Bootstrap Method for the Generalized Autoregressive Moving Average Model. The focus is on the Moving Block Bootstrap (MBB) resampling scheme with its performance being evaluated through a Monte Carlo study and contrasted to their asymptotic Gaussian counterpart. It is stablished that the aforementioned resampling procedure can generate good estimates of parameters bias and confidence intervals. Though, the results rely heavily on the simulated model parameters and block lengths used in the MBB procedure.
publishDate 2018
dc.date.none.fl_str_mv 2018-06-11
2019-08-11T14:51:56Z
2019-08-11T14:51:56Z
2025-09-08T23:30:16Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1843/BIRC-BB4NX5
url https://hdl.handle.net/1843/BIRC-BB4NX5
dc.language.iso.fl_str_mv eng
language eng
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 Federal de Minas Gerais
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
repository.name.fl_str_mv Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)
repository.mail.fl_str_mv repositorio@ufmg.br
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