Bayesian quantile additive regression trees

Autor:	Kindo, Bereket P., Wang, Hao, Hanson, Timothy, Peña, Edsel A.
Rok vydání:	2016
Předmět:	Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 23 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1607.02676 Zobrazit plný text záznamu View this record from Arxiv