Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1080/02664763.2018.1492527 http://hdl.handle.net/11449/184292 |
Resumo: | In this work, we assume that the sequence recording whether or not an ozone exceedance of an environmental threshold has occurred in a given day is ruled by a non-homogeneous Markov chain of order one. In order to account for the possible presence of cycles in the empirical transition probabilities, a parametric form incorporating seasonal components is considered. Results show that even though some covariates (namely, relative humidity and temperature) are not included explicitly in the model, their influence is captured in the behavior of the transition probabilities. Parameters are estimated using the Bayesian point of view via Markov chain Monte Carlo algorithms. The model is applied to ozone data obtained from the monitoring network of Mexico City, Mexico. An analysis of how the methodology could be used as an aid in the decision-making is also given. |
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Repositório Institucional da UNESP |
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Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone dataSeasonal transition probabilitiesBayesian inferenceMarkov chain Monte Carlo algorithmsair pollutionMexico CityIn this work, we assume that the sequence recording whether or not an ozone exceedance of an environmental threshold has occurred in a given day is ruled by a non-homogeneous Markov chain of order one. In order to account for the possible presence of cycles in the empirical transition probabilities, a parametric form incorporating seasonal components is considered. Results show that even though some covariates (namely, relative humidity and temperature) are not included explicitly in the model, their influence is captured in the behavior of the transition probabilities. Parameters are estimated using the Bayesian point of view via Markov chain Monte Carlo algorithms. The model is applied to ozone data obtained from the monitoring network of Mexico City, Mexico. An analysis of how the methodology could be used as an aid in the decision-making is also given.Direccion General de Apoyo al Personal Academico of the Universidad Nacional Autonoma deMexico (UNAM), MexicoUniv Nacl Autonoma Mexico, Inst Matemat, Mexico City, DF, MexicoUniv Estadual Paulista, Fac Ciencias & Tecnol, Pres Prudente, BrazilInst Nacl Ecol & Cambio Climat, Secretaria Medio Ambiente & Recursos Nat, Mexico City, DF, MexicoUniv Estadual Paulista, Fac Ciencias & Tecnol, Pres Prudente, BrazilDireccion General de Apoyo al Personal Academico of the Universidad Nacional Autonoma deMexico (UNAM), Mexico: PAPIIT-IN102416Taylor & Francis LtdUniv Nacl Autonoma MexicoUniversidade Estadual Paulista (Unesp)Inst Nacl Ecol & Cambio ClimatRodrigues, Eliane R.Tarumoto, Mario H. [UNESP]Tzintzun, Guadalupe2019-10-04T11:56:28Z2019-10-04T11:56:28Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article395-415http://dx.doi.org/10.1080/02664763.2018.1492527Journal Of Applied Statistics. Abingdon: Taylor & Francis Ltd, v. 46, n. 3, p. 395-415, 2019.0266-4763http://hdl.handle.net/11449/18429210.1080/02664763.2018.1492527WOS:000456602500002Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal Of Applied Statisticsinfo:eu-repo/semantics/openAccess2021-10-22T22:23:55Zoai:repositorio.unesp.br:11449/184292Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:50:58.728666Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data |
title |
Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data |
spellingShingle |
Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data Rodrigues, Eliane R. Seasonal transition probabilities Bayesian inference Markov chain Monte Carlo algorithms air pollution Mexico City |
title_short |
Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data |
title_full |
Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data |
title_fullStr |
Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data |
title_full_unstemmed |
Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data |
title_sort |
Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data |
author |
Rodrigues, Eliane R. |
author_facet |
Rodrigues, Eliane R. Tarumoto, Mario H. [UNESP] Tzintzun, Guadalupe |
author_role |
author |
author2 |
Tarumoto, Mario H. [UNESP] Tzintzun, Guadalupe |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Univ Nacl Autonoma Mexico Universidade Estadual Paulista (Unesp) Inst Nacl Ecol & Cambio Climat |
dc.contributor.author.fl_str_mv |
Rodrigues, Eliane R. Tarumoto, Mario H. [UNESP] Tzintzun, Guadalupe |
dc.subject.por.fl_str_mv |
Seasonal transition probabilities Bayesian inference Markov chain Monte Carlo algorithms air pollution Mexico City |
topic |
Seasonal transition probabilities Bayesian inference Markov chain Monte Carlo algorithms air pollution Mexico City |
description |
In this work, we assume that the sequence recording whether or not an ozone exceedance of an environmental threshold has occurred in a given day is ruled by a non-homogeneous Markov chain of order one. In order to account for the possible presence of cycles in the empirical transition probabilities, a parametric form incorporating seasonal components is considered. Results show that even though some covariates (namely, relative humidity and temperature) are not included explicitly in the model, their influence is captured in the behavior of the transition probabilities. Parameters are estimated using the Bayesian point of view via Markov chain Monte Carlo algorithms. The model is applied to ozone data obtained from the monitoring network of Mexico City, Mexico. An analysis of how the methodology could be used as an aid in the decision-making is also given. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-04T11:56:28Z 2019-10-04T11:56:28Z 2019-01-01 |
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://dx.doi.org/10.1080/02664763.2018.1492527 Journal Of Applied Statistics. Abingdon: Taylor & Francis Ltd, v. 46, n. 3, p. 395-415, 2019. 0266-4763 http://hdl.handle.net/11449/184292 10.1080/02664763.2018.1492527 WOS:000456602500002 |
url |
http://dx.doi.org/10.1080/02664763.2018.1492527 http://hdl.handle.net/11449/184292 |
identifier_str_mv |
Journal Of Applied Statistics. Abingdon: Taylor & Francis Ltd, v. 46, n. 3, p. 395-415, 2019. 0266-4763 10.1080/02664763.2018.1492527 WOS:000456602500002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal Of Applied Statistics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
395-415 |
dc.publisher.none.fl_str_mv |
Taylor & Francis Ltd |
publisher.none.fl_str_mv |
Taylor & Francis Ltd |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128425377923072 |