Bayesian Variable Selection in Markov Mixture Models

Roberta Paroli, Luigi Spezia

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

9 Citazioni (Scopus)


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.
Lingua originaleEnglish
pagine (da-a)25-47
Numero di pagine23
Stato di pubblicazionePubblicato - 2008


  • Gibbs variable selection
  • Kuo-Mallick method
  • Metropolized-Kuo-Mallick method
  • Stochastic search variable selection

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