title: Energy bagging tree.
abstract: This paper introduces Energy Bagging Tree (EBT) for multivariate nonparametric regression problems. The EBT makes use of a measure of dispersion constructed from a generalized Gini's mean difference as node impurity, and the tree split function therefore corresponds to the product of energy distance and descendants' proportions. As a non-parametric extension of the between-sample variation in the analysis of variance, this measure of dispersion serves well for EBT in understanding certain complex data. Extensive simulation studies indicate that EBT is highly competitive with existing regression tree methods. We also assess the performance of the EBT through a real data analysis on forest fires.
authors.affiliation: Southern China Research Center of Statistical Science, School of Mathematics and Computational Science,Sun Yat-Sen University,Guangzhou, 510275, China.
authors.forename: Taoyun
authors.initials: T
authors.lastname: Cao
authors.affiliation: Southern China Research Center of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, 510275, China; Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
authors.forename: Xueqin
authors.initials: X
authors.lastname: Wang
authors.affiliation: Southern China Research Center of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, 510275, China; Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520-8034, USA.
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
keywords: Energy bagging tree; Energy distance; Generalized Gini's mean difference; Multivariate nonparametric regression