Bayesian methods for variable selection and model choice have become increasingly popular in recent years, due to advances in Markov chain Monte Carlo (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, both when the complexity of the model is high, as in MMM, and when the exogenous variables are strongly correlated. Numerical comparisons among the competing MCMC algorithms are also presented via simulation examples.

Paroli, R., Spezia, L., Bayesian Variable Selection in Markov Mixture Models, <<COMMUNICATIONS IN STATISTICS. SIMULATION AND COMPUTATION>>, 2008; (37): 25-47. [doi:10.1080/03610910701459956] [http://hdl.handle.net/10807/1680]

Bayesian Variable Selection in Markov Mixture Models

Paroli, Roberta;Spezia, Luigi
2008

Abstract

Bayesian methods for variable selection and model choice have become increasingly popular in recent years, due to advances in Markov chain Monte Carlo (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, both when the complexity of the model is high, as in MMM, and when the exogenous variables are strongly correlated. Numerical comparisons among the competing MCMC algorithms are also presented via simulation examples.
Inglese
Paroli, R., Spezia, L., Bayesian Variable Selection in Markov Mixture Models, <<COMMUNICATIONS IN STATISTICS. SIMULATION AND COMPUTATION>>, 2008; (37): 25-47. [doi:10.1080/03610910701459956] [http://hdl.handle.net/10807/1680]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/1680
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