Parametric Quantile Regression Models for Fitting Double Bounded Response with Application to COVID-19 Mortality Rate Data
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Date
Authors
VENEGAS TORRES, OSVALDO
Gallardo, Diego Ignacio
Bourguignon, Marcelo
Gómez, Yolanda M.
Caamaño-Carrillo, Christian
Venegas, Osvaldo
Gallardo, Diego Ignacio
Bourguignon, Marcelo
Gómez, Yolanda M.
Caamaño-Carrillo, Christian
Venegas, Osvaldo
Authors
Date
Datos de publicación:
10.3390/math10132249
Keywords
Covid-19 - Parametric Quantile Regression - Power Johnson Sb Distribution - Proportion
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Abstract
In this paper, we develop two fully parametric quantile regression models, based on the power Johnson S<inf>B</inf> distribution for modeling unit interval response in different quantiles. In particular, the conditional distribution is modeled by the power Johnson S<inf>B</inf> 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. © 2022 Elsevier B.V., All rights reserved.
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Keywords
Covid-19 , Parametric Quantile Regression , Power Johnson Sb Distribution , Proportion
Citation
10.3390/math10132249