  Ensemble of regression trees have become popular statistical tools for the
estimation of conditional mean given a set of predictors. However, quantile
regression trees and their ensembles have not yet garnered much attention
despite the increasing popularity of the linear quantile regression model. This
work proposes a Bayesian quantile additive regression trees model that shows
very good predictive performance illustrated using simulation studies and real
data applications. Further extension to tackle binary classification problems
is also considered.
