Statistics Papers

Document Type

Journal Article

Date of this Version

1998

Publication Source

Journal of the American Statistical Association

Volume

93

Issue

443

Start Page

935

Last Page

948

DOI

10.1080/01621459.1998.10473750

Abstract

In this article we put forward a Bayesian approach for finding classification and regression tree (CART) models. The two basic components of this approach consist of prior specification and stochastic search. The basic idea is to have the prior induce a posterior distribution that will guide the stochastic search toward more promising CART models. As the search proceeds, such models can then be selected with a variety of criteria, such as posterior probability, marginal likelihood, residual sum of squares or misclassification rates. Examples are used to illustrate the potential superiority of this approach over alternative methods.

Copyright/Permission Statement

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 17 Feb 2012, available online: http://wwww.tandfonline.com/10.1080/01621459.1998.10473750.

Keywords

binary trees, Markov chain Monte Carlo, mixture models, model selection, model uncertainty, stochastic search

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Date Posted: 27 November 2017