On a new extreme value distribution : characterization, parametric quantile regression, and application to extreme air pollution events

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Autor(es): dc.contributorUniversidade de Brasília, Departament of Statistics-
Autor(es): dc.contributorUniversidade de Brasília, Departament of Statistics-
Autor(es): dc.contributorUniversidade de Brasília, Departament of Statistics-
Autor(es): dc.contributorUniversidade Federal do Amazonas, Departament of Statistics-
Autor(es): dc.contributorPontificia Universidad Católica de Valparaíso, School of Industrial Engineering-
Autor(es): dc.contributorSan Diego State University, Department of Geography-
Autor(es): dc.creatorSantos, Helton Saulo Bezerra dos-
Autor(es): dc.creatorVila, Roberto-
Autor(es): dc.creatorBittencourt, Verônica Lelis-
Autor(es): dc.creatorLeão, Jeremias-
Autor(es): dc.creatorLeiva, Víctor-
Autor(es): dc.creatorChristakos, George-
Data de aceite: dc.date.accessioned2024-10-23T15:49:25Z-
Data de disponibilização: dc.date.available2024-10-23T15:49:25Z-
Data de envio: dc.date.issued2023-09-29-
Data de envio: dc.date.issued2023-09-29-
Data de envio: dc.date.issued2022-11-05-
Fonte completa do material: dc.identifierhttp://repositorio2.unb.br/jspui/handle/10482/46572-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/s00477-022-02318-8-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/892896-
Descrição: dc.descriptionExtreme-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 We illustrate the introduced methodology with unpublished real air pollution data-
Descrição: dc.descriptionInstituto de Ciências Exatas (IE)-
Descrição: dc.descriptionDepartamento de Estatística (IE EST)-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationhttps://link.springer.com/article/10.1007/s00477-022-02318-8-
Direitos: dc.rightsAcesso Restrito-
Palavras-chave: dc.subjectDistribuições de valores extremos-
Palavras-chave: dc.subjectSimulação Monte Carlo-
Palavras-chave: dc.subjectRegressão de quantis-
Título: dc.titleOn a new extreme value distribution : characterization, parametric quantile regression, and application to extreme air pollution events-
Tipo de arquivo: dc.typelivro digital-
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