Bayesian quantile additive regression trees
Machine Learning
2016-07-12 v1
Abstract
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.
Cite
@article{arxiv.1607.02676,
title = {Bayesian quantile additive regression trees},
author = {Bereket P. Kindo and Hao Wang and Timothy Hanson and Edsel A. Peña},
journal= {arXiv preprint arXiv:1607.02676},
year = {2016}
}
Comments
23 pages, 3 figures