A new quantile regression model for modeling child mortality

This paper considers and studies a distinct special case of omega distribution defined on
the unit interval (0, 1), called unit-omega distribution. Thanks to its simple form, some
of its basic properties are derived. The maximum likelihood method, Bayes method, and
the method of moments are used to estimate the parameters of unit-omega distribution.
These estimation methods are examined by conducting a simulation study. More importantly, the quantile function of unit-omega distribution has a closed-form expression that
allows modeling the conditional quantiles of a unit response variable as a function of
covariates. Residual analysis is performed using randomized quantile residuals and Cox–
Snell residuals. The proposed approach is used to model the quantiles of child mortality
rates, conditional on covariates. These covariates represent the proportions of people left
behind across three key indicators: nutrition, availability of safe drinking sources and
adequate education. Another application that relates to recovery rates of viable CD34+
cell is presented. From both applications, the fitting results of the proposed regression
model outperform those of beta, Kumaraswamy and unit-Weibull regression models.