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Parametric Quantile Regression using Quantile-based Asymmetric Family of the Distributions

Mst. Bithi Akter, Md. Rezaul Karim


Quantile regression is used for finding the effects of covariates on different quantiles of the response variable. The contribution of this article is to develop a theory of quantile regression using the Quantile-based asymmetric (QBA) family (similar to GAMLSS for mean regression) and then apply the proposed approach for analyzing COVID-19 data. More precisely, we estimate regression quantiles and investigate the asymptotic estimators under a generalized linear modeling framework, but the main focus is the quantile estimate rather than the mean estimate. A COVID-19 dataset is used to illustrate the proposed methodology


Quantile-based asymmetric family, Maximum likelihood estimation, Quantile estimation

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