As one of the important statistical methods, quantile regression (QR) extends traditional regression analysis. In QR, various quantiles of the response variable are modeled as linear functions of the ...

Quantile regression offers a versatile framework for characterising the full conditional distribution of a response variable by modelling specified quantiles rather than the mean alone. This approach ...

This study aims to identify the best model of low birth weight by applying and comparing several methods based on the quantile regression method's modification. The birth weight data is violated with ...

Quantile regression models the relationship between a specified conditional quantile (or percentile) of a dependent (response) variable and one or more independent (explanatory) variables (Cade and ...

Abstract: The quantile regression (QR) approach is an improved variant of the least squares method. The study explains why quantile regression outperforms linear regression. It is frequently employed ...

Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of ...

Abstract: We provide an algorithm to build quantile regression trees in O(N log N) time, where N is the number of instances in the training set. Quantile regression trees are regression trees that ...