Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data

Detalhes bibliográficos
Autor(a) principal: Rodrigues, Eliane R.
Data de Publicação: 2019
Outros Autores: Tarumoto, Mario H. [UNESP], Tzintzun, Guadalupe
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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spelling 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
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