Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series
Main Author: | |
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Publication Date: | 2019 |
Other Authors: | , |
Format: | Article |
Language: | eng |
Source: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Download full: | https://hdl.handle.net/10216/119750 |
Summary: | This work investigates outlier detection and modelling in non-Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets. |
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Bayesian Outlier Detection in Non-Gaussian Autoregressive Time SeriesThis work investigates outlier detection and modelling in non-Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets.20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/119750eng0143-978210.1111/jtsa.12439Maria Eduarda SilvaPereira, IMcCabe, Binfo: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:RCAAP2023-11-29T15:04:19Zoai:repositorio-aberto.up.pt:10216/119750Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:14:57.459622Repositório 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 |
Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series |
title |
Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series |
spellingShingle |
Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series Maria Eduarda Silva |
title_short |
Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series |
title_full |
Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series |
title_fullStr |
Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series |
title_full_unstemmed |
Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series |
title_sort |
Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series |
author |
Maria Eduarda Silva |
author_facet |
Maria Eduarda Silva Pereira, I McCabe, B |
author_role |
author |
author2 |
Pereira, I McCabe, B |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Maria Eduarda Silva Pereira, I McCabe, B |
description |
This work investigates outlier detection and modelling in non-Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/119750 |
url |
https://hdl.handle.net/10216/119750 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0143-9782 10.1111/jtsa.12439 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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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 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136070130991104 |