Date of this Version
Statistics and Computing
The Bayesian CART (classification and regression tree) approach proposed by Chipman, George and McCulloch (1998) entails putting a prior distribution on the set of all CART models and then using stochastic search to select a model. The main thrust of this paper is to propose a new class of hierarchical priors which enhance the potential of this Bayesian approach. These priors indicate a preference for smooth local mean structure, resulting in tree models which shrink predictions from adjacent terminal node towards each other. Past methods for tree shrinkage have searched for trees without shrinking, and applied shrinkage to the identified tree only after the search. By using hierarchical priors in the stochastic search, the proposed method searches for shrunk trees that fit well and improves the tree through shrinkage of predictions.
The final publication is available at Springer via http://dx.doi.org/ 10.1023/A:1008980332240.
binary trees, tree shrinkage, Markov chain Monte Carlo, model selection, stochastic search, mixture models
Chipman, H. A., George, E. I., & McCulloch, R. E. (2000). Hierarchical Priors for Bayesian CART Shrinkage. Statistics and Computing, 10 (1), 17-24. http://dx.doi.org/10.1023/A:1008980332240
Date Posted: 27 November 2017
This document has been peer reviewed.