Title:	On a new extreme value distribution : characterization, parametric quantile regression, and application to extreme air pollution events
Authors:	Santos, Helton Saulo Bezerra dos Vila, Roberto Bittencourt, Verônica Lelis Leão, Jeremias Leiva, Víctor Christakos, George
metadata.dc.contributor.affiliation:	Universidade de Brasília, Departament of Statistics Universidade de Brasília, Departament of Statistics Universidade de Brasília, Departament of Statistics Universidade Federal do Amazonas, Departament of Statistics Pontificia Universidad Católica de Valparaíso, School of Industrial Engineering San Diego State University, Department of Geography
Assunto::	Distribuições de valores extremos Simulação Monte Carlo Regressão de quantis
Issue Date:	6-Nov-2022
Publisher:	Springer
Citation:	SAULO, 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.
Abstract:	Extreme-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 We illustrate the introduced methodology with unpublished real air pollution data
metadata.dc.description.unidade:	Instituto de Ciências Exatas (IE) Departamento de Estatística (IE EST)
DOI:	https://doi.org/10.1007/s00477-022-02318-8
metadata.dc.relation.publisherversion:	https://link.springer.com/article/10.1007/s00477-022-02318-8
Appears in Collections:	Artigos publicados em periódicos e afins

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