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Universidade de Brasília
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dc.contributor.authorSantos, Helton Saulo Bezerra dos-
dc.contributor.authorVila, Roberto-
dc.contributor.authorBittencourt, Verônica Lelis-
dc.contributor.authorLeão, Jeremias-
dc.contributor.authorLeiva, Víctor-
dc.contributor.authorChristakos, George-
dc.date.accessioned2023-09-29T10:55:03Z-
dc.date.available2023-09-29T10:55:03Z-
dc.date.issued2022-11-06-
dc.identifier.citationSAULO, Helton et al. On a new extreme value distribution: characterization, parametric quantile regression, and application to extreme air pollution events. Stochastic Environmental Research and Risk Assessment, v. 37, p. 1119-1136, 2023. DOI: https://doi.org/10.1007/s00477-022-02318-8. Disponível em: https://link.springer.com/article/10.1007/s00477-022-02318-8.pt_BR
dc.identifier.urihttp://repositorio2.unb.br/jspui/handle/10482/46572-
dc.language.isoengpt_BR
dc.publisherSpringerpt_BR
dc.rightsAcesso Restritopt_BR
dc.titleOn a new extreme value distribution : characterization, parametric quantile regression, and application to extreme air pollution eventspt_BR
dc.typeArtigopt_BR
dc.subject.keywordDistribuições de valores extremospt_BR
dc.subject.keywordSimulação Monte Carlopt_BR
dc.subject.keywordRegressão de quantispt_BR
dc.identifier.doihttps://doi.org/10.1007/s00477-022-02318-8pt_BR
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s00477-022-02318-8pt_BR
dc.description.abstract1Extreme-value distributions are important when modeling weather events, such as temperature and rainfall. These dis- tributions are also important for modeling air pollution events. Particularly, the extreme-value Birnbaum-Saunders regression is a helpful tool in the modeling of extreme events. However, this model is implemented by adding covariates to the location parameter. Given the importance of quantile regression to estimate the effects of covariates along the wide spectrum of a response variable, we introduce a quantile extreme-value Birnbaum-Saunders distribution and its corre- sponding quantile regression model. We implement a likelihood-based approach for parameter estimation and consider two types of statistical residuals. A Monte Carlo simulation is performed to assess the behavior of the estimation method and the empirical distribution of the residuals. We illustrate the introduced methodology with unpublished real air pollution datapt_BR
dc.contributor.affiliationUniversidade de Brasília, Departament of Statisticspt_BR
dc.contributor.affiliationUniversidade de Brasília, Departament of Statisticspt_BR
dc.contributor.affiliationUniversidade de Brasília, Departament of Statisticspt_BR
dc.contributor.affiliationUniversidade Federal do Amazonas, Departament of Statisticspt_BR
dc.contributor.affiliationPontificia Universidad Católica de Valparaíso, School of Industrial Engineeringpt_BR
dc.contributor.affiliationSan Diego State University, Department of Geographypt_BR
dc.description.unidadeInstituto de Ciências Exatas (IE)pt_BR
dc.description.unidadeDepartamento de Estatística (IE EST)pt_BR
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