dc.creatorSánchez, Luis
dc.creatorIbacache-Pulgar, Germán
dc.creatorMarchant, Carolina
dc.creatorRiquelme, Marco
dc.date2023-11-30T17:28:49Z
dc.date2023-11-30T17:28:49Z
dc.date2023
dc.date.accessioned2024-05-02T20:31:55Z
dc.date.available2024-05-02T20:31:55Z
dc.identifierhttp://repositorio.ucm.cl/handle/ucm/5107
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9275307
dc.descriptionMany phenomena can be described by random variables that follow asymmetrical distributions. In the context of regression, when the response variable Y follows such a distribution, it is preferable to estimate the response variable for predictor values using the conditional median. Quantile regression models can be employed for this purpose. However, traditional models do not incorporate a distributional assumption for the response variable. To introduce a distributional assumption while preserving model flexibility, we propose new varying-coefficients quantile regression models based on the family of log-symmetric distributions. We achieve this by reparametrizing the distribution of the response variable using quantiles. Parameter estimation is performed using a maximum likelihood penalized method, and a back-fitting algorithm is developed. Additionally, we propose diagnostic techniques to identify potentially influential local observations and leverage points. Finally, we apply and illustrate the methodology using real pollution data from Padre Las Casas city, one of the most polluted cities in Latin America and the Caribbean according to the World Air Quality Index Ranking.
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceAxioms, 12(10), 976
dc.subjectLocal influence techniques
dc.subjectLog-symmetric distributions family
dc.subjectPM2.5 levels
dc.subjectQuantile regression
dc.subjectSemiparametric models
dc.titleModeling environmental pollution using varying-coefficients quantile regression models under log-symmetric distributions
dc.typeArticle


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