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.
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