Date of this Version
Journal of the American Statistical Association
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.
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.
binary trees, Markov chain Monte Carlo, mixture models, model selection, model uncertainty, stochastic search
Chipman, H. A., George, E. I., & McCulloch, R. E. (1998). Bayesian CART Model Search. Journal of the American Statistical Association, 93 (443), 935-948. http://dx.doi.org/10.1080/01621459.1998.10473750
Date Posted: 27 November 2017