Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
DOI: | 10.1007/978-3-319-05323-3-23 |
Texto Completo: | http://hdl.handle.net/10174/13940 https://doi.org/10.1007/978-3-319-05323-3-23 |
Resumo: | Within the context of accessing the risk of forest fires, Amaral-Turkman et al. [1] have proposed a spatio-temporal hierarchical approach which jointly models the fire ignition probability and the fire’s size, in a Bayesian framework. This is recovered and applied to Portuguese forest fires data, with some necessary modifications in what concerns the format of the data (not available in a regular lattice over the territory) and also because of the estimation complications that arise due the high dimensionality of the neighbouring structure involved. To address the latter, as it compromises the estimation via Markov Chain Monte Carlo (MCMC) methods, and having the model be recognized as a latent Gaussian model, it was chosen to do the Bayesian estimation also using an Integrated Nested Laplace Approximation approach, with real computational advantages. Corresponding methodologies and results are described and compared. |
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7160 |
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Using INLA to estimate a highly dimensional spatial model for forest fires in PortugalINLASpatial ModelWithin the context of accessing the risk of forest fires, Amaral-Turkman et al. [1] have proposed a spatio-temporal hierarchical approach which jointly models the fire ignition probability and the fire’s size, in a Bayesian framework. This is recovered and applied to Portuguese forest fires data, with some necessary modifications in what concerns the format of the data (not available in a regular lattice over the territory) and also because of the estimation complications that arise due the high dimensionality of the neighbouring structure involved. To address the latter, as it compromises the estimation via Markov Chain Monte Carlo (MCMC) methods, and having the model be recognized as a latent Gaussian model, it was chosen to do the Bayesian estimation also using an Integrated Nested Laplace Approximation approach, with real computational advantages. Corresponding methodologies and results are described and compared.Springer International Publishing2015-03-31T16:50:58Z2015-03-312014-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/13940http://hdl.handle.net/10174/13940https://doi.org/10.1007/978-3-319-05323-3-23engNew Advances in Statistical Modeling and Applicationsicn@fct.unl.ptmmo@uevora.ptsmarques.isa.ulisboa.pt340Natário, IsabelOliveira, ManuelaMarques, Suseteinfo: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:RCAAP2024-01-03T18:49:04Zoai:dspace.uevora.pt:10174/13940Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:02:34.108758Repositório 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 |
Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal |
title |
Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal |
spellingShingle |
Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal Natário, Isabel INLA Spatial Model Natário, Isabel INLA Spatial Model |
title_short |
Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal |
title_full |
Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal |
title_fullStr |
Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal |
title_full_unstemmed |
Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal |
title_sort |
Using INLA to estimate a highly dimensional spatial model for forest fires in Portugal |
author |
Natário, Isabel |
author_facet |
Natário, Isabel Natário, Isabel Oliveira, Manuela Marques, Susete Oliveira, Manuela Marques, Susete |
author_role |
author |
author2 |
Oliveira, Manuela Marques, Susete |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Natário, Isabel Oliveira, Manuela Marques, Susete |
dc.subject.por.fl_str_mv |
INLA Spatial Model |
topic |
INLA Spatial Model |
description |
Within the context of accessing the risk of forest fires, Amaral-Turkman et al. [1] have proposed a spatio-temporal hierarchical approach which jointly models the fire ignition probability and the fire’s size, in a Bayesian framework. This is recovered and applied to Portuguese forest fires data, with some necessary modifications in what concerns the format of the data (not available in a regular lattice over the territory) and also because of the estimation complications that arise due the high dimensionality of the neighbouring structure involved. To address the latter, as it compromises the estimation via Markov Chain Monte Carlo (MCMC) methods, and having the model be recognized as a latent Gaussian model, it was chosen to do the Bayesian estimation also using an Integrated Nested Laplace Approximation approach, with real computational advantages. Corresponding methodologies and results are described and compared. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-11-01T00:00:00Z 2015-03-31T16:50:58Z 2015-03-31 |
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.uri.fl_str_mv |
http://hdl.handle.net/10174/13940 http://hdl.handle.net/10174/13940 https://doi.org/10.1007/978-3-319-05323-3-23 |
url |
http://hdl.handle.net/10174/13940 https://doi.org/10.1007/978-3-319-05323-3-23 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
New Advances in Statistical Modeling and Applications icn@fct.unl.pt mmo@uevora.pt smarques.isa.ulisboa.pt 340 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Springer International Publishing |
publisher.none.fl_str_mv |
Springer International Publishing |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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 |
repository.mail.fl_str_mv |
|
_version_ |
1822243938257338368 |
dc.identifier.doi.none.fl_str_mv |
10.1007/978-3-319-05323-3-23 |