Time series of counts under censoring: a Bayesian approach

Detalhes bibliográficos
Autor(a) principal: Silva, Isabel
Data de Publicação: 2023
Outros Autores: Silva, Maria Eduarda, Pereira, Isabel, McCabe, Brendan
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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spelling 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
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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
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dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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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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