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arXiv · 2103.07039

Parametric quantile regression models for fitting double bounded response with application to COVID-19 mortality rate data

Diego I. Gallardo·Marcelo Bourguignon·Yolanda M. Gómez·Christian Caamaño-Carrillo
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Abstract

In this paper, we develop two fully parametric quantile regression models, based on power Johnson SB distribution Cancho et al. (2020), for modeling unit interval response at different quantiles. In particular, the conditional distribution is modelled by the power Johnson SB distribution. The maximum likelihood method is employed to estimate the model parameters. Simulation studies are conducted to evaluate the performance of the maximum likelihood estimators in finite samples. Furthermore, we discuss residuals and influence diagnostic tools. The effectiveness of our proposals is illustrated with two data set given by the mortality rate of COVID-19 in different countries.

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Diego I. Gallardo, Marcelo Bourguignon, Yolanda M. Gómez, Christian Caamaño-Carrillo. 2021-03-12. Parametric quantile regression models for fitting double bounded response with application to COVID-19 mortality rate data. https://arxiv.org/abs/2103.07039

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