Modelling preferential sampling in time

Detalhes bibliográficos
Autor(a) principal: Monteiro, Andreia
Data de Publicação: 2019
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/10773/35236
Resumo: Preferential sampling in time occurs when there is stochastic dependence between the process being modeled and the times of the observations. Examples occur in fisheries if the data are observed when the resource is available, in sensoring when sensors keep only some records in order to save memory and in clinical studies, when a worse clinical condition leads to more frequent observations of the patient. In all such situations the observation times are informative on the underlying process. To make inference in time series observed under Preferential Sampling we propose, in this work, a numerical method based on a Laplace approach to optimize the likelihood and to approximate the underlying process we adopt a technique based on stochastic partial differential equation. Numerical studies with simulated and real data sets are performed to illustrate the benefits of the proposed approach.
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spelling Modelling preferential sampling in timeContinuous time autoregressive processLaplacePreferential samplingSPDETime seriesPreferential sampling in time occurs when there is stochastic dependence between the process being modeled and the times of the observations. Examples occur in fisheries if the data are observed when the resource is available, in sensoring when sensors keep only some records in order to save memory and in clinical studies, when a worse clinical condition leads to more frequent observations of the patient. In all such situations the observation times are informative on the underlying process. To make inference in time series observed under Preferential Sampling we propose, in this work, a numerical method based on a Laplace approach to optimize the likelihood and to approximate the underlying process we adopt a technique based on stochastic partial differential equation. Numerical studies with simulated and real data sets are performed to illustrate the benefits of the proposed approach.Sociedad de Estadistica e Investigacion Operativa2022-11-21T15:52:20Z2019-01-01T00:00:00Z2019info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/35236eng1889-3805Monteiro, 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:RCAAP2024-02-22T12:07:42Zoai:ria.ua.pt:10773/35236Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:06:15.043121Repositório 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 preferential sampling in time
title Modelling preferential sampling in time
spellingShingle Modelling preferential sampling in time
Monteiro, Andreia
Continuous time autoregressive process
Laplace
Preferential sampling
SPDE
Time series
title_short Modelling preferential sampling in time
title_full Modelling preferential sampling in time
title_fullStr Modelling preferential sampling in time
title_full_unstemmed Modelling preferential sampling in time
title_sort Modelling preferential sampling in time
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 Continuous time autoregressive process
Laplace
Preferential sampling
SPDE
Time series
topic Continuous time autoregressive process
Laplace
Preferential sampling
SPDE
Time series
description Preferential sampling in time occurs when there is stochastic dependence between the process being modeled and the times of the observations. Examples occur in fisheries if the data are observed when the resource is available, in sensoring when sensors keep only some records in order to save memory and in clinical studies, when a worse clinical condition leads to more frequent observations of the patient. In all such situations the observation times are informative on the underlying process. To make inference in time series observed under Preferential Sampling we propose, in this work, a numerical method based on a Laplace approach to optimize the likelihood and to approximate the underlying process we adopt a technique based on stochastic partial differential equation. Numerical studies with simulated and real data sets are performed to illustrate the benefits of the proposed approach.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01T00:00:00Z
2019
2022-11-21T15:52:20Z
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language eng
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dc.publisher.none.fl_str_mv Sociedad de Estadistica e Investigacion Operativa
publisher.none.fl_str_mv Sociedad de Estadistica e Investigacion Operativa
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