Unit Gamma/Gompertz Quantile Regression with Applications to Skewed Data

Abstract

In this study, new unit quantile regression model, called the Unit Gamma/ Gompertz quantile regression for bounded responses is developed by re-parameterizing the Unit Gamma/Gompertz distribution. To estimate the parameters of the new quantile regression model, the maximum likelihood approach is used to develop estimators for the parameters. Monte Carlo simulations are used to test the consistency of the maximum likelihood estimators for the parameters of the new quantile regression model. The application of the new quantile regression model is illustrated using three real life datasets and the results revealed that the Unit Gamma/Gompertz quantile regression performs better than the beta regression model when the unit response variable has skewed observations and outliers.