Bayesian quantile additive regression trees
This work addresses a gap in statistical tools for quantile regression using tree-based methods, offering a novel approach for researchers and practitioners in fields requiring robust predictive modeling.
The paper tackles the lack of attention to quantile regression trees and their ensembles by proposing a Bayesian quantile additive regression trees model, which demonstrates very good predictive performance in simulation studies and real data applications, with an extension to binary classification problems.
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.