Bayesian Variable Selection in Markov Mixture Models

Roberta Paroli (1) , Luigi Spezia (2)

1 Statistics
2 Statistics

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.

Mots clés: Physical Sciences

Roberta Paroli, Luigi Spezia. Bayesian Variable Selection in Markov Mixture Models. Communications in Statistics - Simulation and Computation, 2008, 37 (01), pp.25-47. ⟨10.1080/03610910701459956⟩. ⟨hal-00514318⟩