Parametric Quantile Regression Models for Fitting Double Bounded Response with Application to COVID-19 Mortality Rate Data
Parametric Quantile Regression Models for Fitting Double Bounded Response with Application to COVID-19 Mortality Rate Data
Authors
Gallardo, Diego, I
Bourguignon, Marcelo
Gomez, Yolanda M.
Caamano Carrillo, Christian
Venegas, Osvaldo
Bourguignon, Marcelo
Gomez, Yolanda M.
Caamano Carrillo, Christian
Venegas, Osvaldo
Profesor GuĆa
Authors
Date
Datos de publicaciĆ³n:
10.3390/math10132249
MATHEMATICS,Vol.10,2022
MATHEMATICS,Vol.10,2022
Tipo de recurso
Article
Keywords
Materia geogrƔfica
Collections
Abstract
In this paper, we develop two fully parametric quantile regression models, based on the power Johnson S-B distribution for modeling unit interval response in different quantiles. In particular, the conditional distribution is modeled by the power Johnson S-B distribution. The maximum likelihood (ML) estimation method is employed to estimate the model parameters. Simulation studies are conducted to evaluate the performance of the ML estimators in finite samples. Furthermore, we discuss influence diagnostic tools and residuals. The effectiveness of our proposals is illustrated with a data set of the mortality rate of COVID-19 in different countries. The results of our models with this data set show the potential of using the new methodology. Thus, we conclude that the results are favorable to the use of proposed quantile regression models for fitting double bounded data.