%0 Journal Article
%T MODELOS DE ¨˘RBOL DE REGRESI¨®N BAYESIANO: UN ESTUDIO DE CASO
%A O. Ju¨˘rez* y E.Castells**
%J Revista Investigaci¨®n Operacional
%D 2010
%I Universidad de La Habana
%X The Bayesian method for selecting regression models proposed by Chipman et al. (1998a) as well as some related results of the same authors are explored. In applications of this method, forming groups of the generated models has resulted a very useful tool (models are generated by Monte Carlo Markov Chains) so some metrics are required and here we propose a new one for grouping the models. The possibility of reducing the number of necessaries models to be generated in order to determine (with Chipman et al. (1998a) approach) a tree which give a satisfactory explanation of the data is explored by means of a simulation study. We apply the method to the data used by Denison et al. (1998) and some comparisons are made. Lastly we analyze the results of the application to the data of some socio-economical study.
%K Monte Carlo Markov Chain
%K Regression tree
%K Metric on Models
%K Log. Likelihood
%K Integrated Likelihood.
%U http://rev-inv-ope.univ-paris1.fr/files/31210/31210-02R.pdf