State space models with spatial deformation
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/jspui/handle/123456789/27480 |
Resumo: | Space deformation has been proposed to model space-time varying observation processes with non-stationary spatial covariance structure under the hypothesis of temporal stationarity. In real applications, however, the temporal stationarity assumption is inappropriate and unrealistic. In thisworkwe propose a spatialtemporal model whose temporal trend is modeled through state space models and a spatially varying anisotropy is modeled through spatial deformation, under the Bayesian approach. A distinctive feature of our approach is the consideration of model uncertainty in an unified framework. Our model has a clear advantage over the ones proposed so far in the literature when themain objective of the study is to perform spatial interpolation for fixed points in time. Approximations of the posterior distributions of the model parameters are obtained via Markov chain Monte Carlo methods. This allows for prediction of the process values in space and time as well as handling of missing values. Two applications are presented: the first one to model concentrations of sulfur dioxide in the eastern United States and the second one to model monthly minimum temperatures in the State of Rio de Janeiro. |
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Morales, Fidel Ernesto CastroGamerman, DaniPaez, Marina Silva2019-08-09T11:51:52Z2019-08-09T11:51:52Z2013CASTRO, Fidel E. M.; GAMERMAN, Dani ; PAEZ, Marina S. . State space models with spatial deformation. Environmental and Ecological Statistics , v. 20, p. 191-214, 2013. Disponível em:<https://link.springer.com/article/10.1007%2Fs10651-012-0215-2>. Acesso em: 06 dez. 2017https://repositorio.ufrn.br/jspui/handle/123456789/2748010.1007engEnvironmental and Ecological StatisticsAnisotropyBayesian inferenceConcentrations of sulfur dioxideMCMCMinimum temperatureSpatial deformationState space modelsState space models with spatial deformationinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleSpace deformation has been proposed to model space-time varying observation processes with non-stationary spatial covariance structure under the hypothesis of temporal stationarity. In real applications, however, the temporal stationarity assumption is inappropriate and unrealistic. In thisworkwe propose a spatialtemporal model whose temporal trend is modeled through state space models and a spatially varying anisotropy is modeled through spatial deformation, under the Bayesian approach. A distinctive feature of our approach is the consideration of model uncertainty in an unified framework. Our model has a clear advantage over the ones proposed so far in the literature when themain objective of the study is to perform spatial interpolation for fixed points in time. Approximations of the posterior distributions of the model parameters are obtained via Markov chain Monte Carlo methods. This allows for prediction of the process values in space and time as well as handling of missing values. Two applications are presented: the first one to model concentrations of sulfur dioxide in the eastern United States and the second one to model monthly minimum temperatures in the State of Rio de Janeiro.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNTEXTStateSpaceModels_2013.pdf.txtStateSpaceModels_2013.pdf.txtExtracted texttext/plain51919https://repositorio.ufrn.br/bitstream/123456789/27480/3/StateSpaceModels_2013.pdf.txt65a1f24d71da7ca35fba7244c97a27abMD53THUMBNAILStateSpaceModels_2013.pdf.jpgStateSpaceModels_2013.pdf.jpgGenerated Thumbnailimage/jpeg1396https://repositorio.ufrn.br/bitstream/123456789/27480/4/StateSpaceModels_2013.pdf.jpg8e9391e419cee959224b2bab462bfa3cMD54ORIGINALStateSpaceModels_2013.pdfStateSpaceModels_2013.pdfapplication/pdf1113951https://repositorio.ufrn.br/bitstream/123456789/27480/1/StateSpaceModels_2013.pdf648094c08fa1ad0755397772b83d1ecaMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ufrn.br/bitstream/123456789/27480/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/274802019-08-11 02:16:10.191oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2019-08-11T05:16:10Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
State space models with spatial deformation |
title |
State space models with spatial deformation |
spellingShingle |
State space models with spatial deformation Morales, Fidel Ernesto Castro Anisotropy Bayesian inference Concentrations of sulfur dioxide MCMC Minimum temperature Spatial deformation State space models |
title_short |
State space models with spatial deformation |
title_full |
State space models with spatial deformation |
title_fullStr |
State space models with spatial deformation |
title_full_unstemmed |
State space models with spatial deformation |
title_sort |
State space models with spatial deformation |
author |
Morales, Fidel Ernesto Castro |
author_facet |
Morales, Fidel Ernesto Castro Gamerman, Dani Paez, Marina Silva |
author_role |
author |
author2 |
Gamerman, Dani Paez, Marina Silva |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Morales, Fidel Ernesto Castro Gamerman, Dani Paez, Marina Silva |
dc.subject.por.fl_str_mv |
Anisotropy Bayesian inference Concentrations of sulfur dioxide MCMC Minimum temperature Spatial deformation State space models |
topic |
Anisotropy Bayesian inference Concentrations of sulfur dioxide MCMC Minimum temperature Spatial deformation State space models |
description |
Space deformation has been proposed to model space-time varying observation processes with non-stationary spatial covariance structure under the hypothesis of temporal stationarity. In real applications, however, the temporal stationarity assumption is inappropriate and unrealistic. In thisworkwe propose a spatialtemporal model whose temporal trend is modeled through state space models and a spatially varying anisotropy is modeled through spatial deformation, under the Bayesian approach. A distinctive feature of our approach is the consideration of model uncertainty in an unified framework. Our model has a clear advantage over the ones proposed so far in the literature when themain objective of the study is to perform spatial interpolation for fixed points in time. Approximations of the posterior distributions of the model parameters are obtained via Markov chain Monte Carlo methods. This allows for prediction of the process values in space and time as well as handling of missing values. Two applications are presented: the first one to model concentrations of sulfur dioxide in the eastern United States and the second one to model monthly minimum temperatures in the State of Rio de Janeiro. |
publishDate |
2013 |
dc.date.issued.fl_str_mv |
2013 |
dc.date.accessioned.fl_str_mv |
2019-08-09T11:51:52Z |
dc.date.available.fl_str_mv |
2019-08-09T11:51:52Z |
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.citation.fl_str_mv |
CASTRO, Fidel E. M.; GAMERMAN, Dani ; PAEZ, Marina S. . State space models with spatial deformation. Environmental and Ecological Statistics , v. 20, p. 191-214, 2013. Disponível em:<https://link.springer.com/article/10.1007%2Fs10651-012-0215-2>. Acesso em: 06 dez. 2017 |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/jspui/handle/123456789/27480 |
dc.identifier.doi.none.fl_str_mv |
10.1007 |
identifier_str_mv |
CASTRO, Fidel E. M.; GAMERMAN, Dani ; PAEZ, Marina S. . State space models with spatial deformation. Environmental and Ecological Statistics , v. 20, p. 191-214, 2013. Disponível em:<https://link.springer.com/article/10.1007%2Fs10651-012-0215-2>. Acesso em: 06 dez. 2017 10.1007 |
url |
https://repositorio.ufrn.br/jspui/handle/123456789/27480 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Environmental and Ecological Statistics |
publisher.none.fl_str_mv |
Environmental and Ecological Statistics |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
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UFRN |
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UFRN |
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