Bayesian Variable Selection in Markov Mixture Models

Bayesian methods for variable selection have become increasingly popular in recent years, due to advances in MCMC computational algorithms. Several methods have been proposed in literature in the case of linear and generalized linear models. In this paper we adapt some of the most popular algorithms to a class of non-linear and non-Gaussian time series models, i.e. the Markov mixture models (MMM). We also propose the "Metropolization" of the algorithm of Kuo and Mallick (1998), in order to tackle variable selection efficiently. Numerical comparisons among the competing MCMC algorithms are also presented via simulation examples. Note: The following files were submitted by the author for peer review, but cannot be converted to PDF. You must view these files (e.g. movies) online. fig2-3.wmf fig2-4.wmf Table1.tex Table2.tex Table3.tex Table4.tex Table5.tex URL: http://mc.manuscriptcentral.com/lssp E-mail: [email protected] Communications in Statistics Simulation and Computation