Modelling irregularly spaced time series under preferential sampling

Detalhes bibliográficos
Autor(a) principal: Monteiro, Andreia
Data de Publicação: 2020
Outros Autores: Menezes, Raquel, Silva, Maria Eduarda
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/30279
Resumo: Irregularly spaced time series are commonly encountered in the analysis of time series. A particular case is that in which the collection procedure over time depends also on the observed values. In such situations, there is stochastic dependence between the process being modeled and the times at which the observations are made. Ignoring this dependence can lead to biased estimates and misleading inferences. In this paper, we introduce the concept of preferential sampling in the temporal dimension and we propose a model to make inference and prediction. The methodology is illustrated using artificial data as well a real data set.
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spelling Modelling irregularly spaced time series under preferential samplingPreferential samplingTime seriesContinuous time autoregressive processSPDEIrregularly spaced time series are commonly encountered in the analysis of time series. A particular case is that in which the collection procedure over time depends also on the observed values. In such situations, there is stochastic dependence between the process being modeled and the times at which the observations are made. Ignoring this dependence can lead to biased estimates and misleading inferences. In this paper, we introduce the concept of preferential sampling in the temporal dimension and we propose a model to make inference and prediction. The methodology is illustrated using artificial data as well a real data set.Instituto Nacional de Estatística2021-01-11T18:53:07Z2020-10-01T00:00:00Z2020-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/30279eng1645-6726Monteiro, AndreiaMenezes, RaquelSilva, Maria Eduardainfo: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:RCAAP2024-02-22T11:58:29Zoai:ria.ua.pt:10773/30279Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:02:24.027937Repositório 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 Modelling irregularly spaced time series under preferential sampling
title Modelling irregularly spaced time series under preferential sampling
spellingShingle Modelling irregularly spaced time series under preferential sampling
Monteiro, Andreia
Preferential sampling
Time series
Continuous time autoregressive process
SPDE
title_short Modelling irregularly spaced time series under preferential sampling
title_full Modelling irregularly spaced time series under preferential sampling
title_fullStr Modelling irregularly spaced time series under preferential sampling
title_full_unstemmed Modelling irregularly spaced time series under preferential sampling
title_sort Modelling irregularly spaced time series under preferential sampling
author Monteiro, Andreia
author_facet Monteiro, Andreia
Menezes, Raquel
Silva, Maria Eduarda
author_role author
author2 Menezes, Raquel
Silva, Maria Eduarda
author2_role author
author
dc.contributor.author.fl_str_mv Monteiro, Andreia
Menezes, Raquel
Silva, Maria Eduarda
dc.subject.por.fl_str_mv Preferential sampling
Time series
Continuous time autoregressive process
SPDE
topic Preferential sampling
Time series
Continuous time autoregressive process
SPDE
description Irregularly spaced time series are commonly encountered in the analysis of time series. A particular case is that in which the collection procedure over time depends also on the observed values. In such situations, there is stochastic dependence between the process being modeled and the times at which the observations are made. Ignoring this dependence can lead to biased estimates and misleading inferences. In this paper, we introduce the concept of preferential sampling in the temporal dimension and we propose a model to make inference and prediction. The methodology is illustrated using artificial data as well a real data set.
publishDate 2020
dc.date.none.fl_str_mv 2020-10-01T00:00:00Z
2020-10
2021-01-11T18:53:07Z
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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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/30279
url http://hdl.handle.net/10773/30279
dc.language.iso.fl_str_mv eng
language eng
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dc.publisher.none.fl_str_mv Instituto Nacional de Estatística
publisher.none.fl_str_mv Instituto Nacional de Estatística
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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