Time series of counts under censoring: a Bayesian approach
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/36634 |
Resumo: | Censored data are frequently found in diverse fields including environmental monitoring, medicine, economics and social sciences. Censoring occurs when observations are available only for a restricted range, e.g., due to a detection limit. Ignoring censoring produces biased estimates and unreliable statistical inference. The aim of this work is to contribute to the modelling of time series of counts under censoring using convolution closed infinitely divisible (CCID) models. The emphasis is on estimation and inference problems, using Bayesian approaches with Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms. |
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Time series of counts under censoring: a Bayesian approachBayesian estimationCensored time seriesConvolution closed infinitely divisiblePoisson INAR(1) modelCensored data are frequently found in diverse fields including environmental monitoring, medicine, economics and social sciences. Censoring occurs when observations are available only for a restricted range, e.g., due to a detection limit. Ignoring censoring produces biased estimates and unreliable statistical inference. The aim of this work is to contribute to the modelling of time series of counts under censoring using convolution closed infinitely divisible (CCID) models. The emphasis is on estimation and inference problems, using Bayesian approaches with Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms.MDPI2023-03-24T12:04:58Z2023-03-23T00:00:00Z2023-03-23info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/36634eng10.3390/e25040549Silva, IsabelSilva, Maria EduardaPereira, IsabelMcCabe, Brendaninfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:10:40Zoai:ria.ua.pt:10773/36634Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:07:23.295870Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Time series of counts under censoring: a Bayesian approach |
title |
Time series of counts under censoring: a Bayesian approach |
spellingShingle |
Time series of counts under censoring: a Bayesian approach Silva, Isabel Bayesian estimation Censored time series Convolution closed infinitely divisible Poisson INAR(1) model |
title_short |
Time series of counts under censoring: a Bayesian approach |
title_full |
Time series of counts under censoring: a Bayesian approach |
title_fullStr |
Time series of counts under censoring: a Bayesian approach |
title_full_unstemmed |
Time series of counts under censoring: a Bayesian approach |
title_sort |
Time series of counts under censoring: a Bayesian approach |
author |
Silva, Isabel |
author_facet |
Silva, Isabel Silva, Maria Eduarda Pereira, Isabel McCabe, Brendan |
author_role |
author |
author2 |
Silva, Maria Eduarda Pereira, Isabel McCabe, Brendan |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Silva, Isabel Silva, Maria Eduarda Pereira, Isabel McCabe, Brendan |
dc.subject.por.fl_str_mv |
Bayesian estimation Censored time series Convolution closed infinitely divisible Poisson INAR(1) model |
topic |
Bayesian estimation Censored time series Convolution closed infinitely divisible Poisson INAR(1) model |
description |
Censored data are frequently found in diverse fields including environmental monitoring, medicine, economics and social sciences. Censoring occurs when observations are available only for a restricted range, e.g., due to a detection limit. Ignoring censoring produces biased estimates and unreliable statistical inference. The aim of this work is to contribute to the modelling of time series of counts under censoring using convolution closed infinitely divisible (CCID) models. The emphasis is on estimation and inference problems, using Bayesian approaches with Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-03-24T12:04:58Z 2023-03-23T00:00:00Z 2023-03-23 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/36634 |
url |
http://hdl.handle.net/10773/36634 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.3390/e25040549 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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