Bayesian Variable Selection in Markov Mixture Models

References

• Brown , P. J. , Vannucci , M. , Fearn , T. ( 1998 ). Multivariate Bayesian variable selection and prediction . Journal of the Royal Statistical Society, Series B 60 : 627 – 641 .
   Google Scholar
• Carlin , B. P. , Chib , S. ( 1995 ). Bayesian model choice via Markov chain Monte Carlo . Journal of the Royal Statistical Society, Series B 57 : 473 – 484 .
   Google Scholar
• Celeux , G. , Hurn , M. , Robert , C. P. ( 2000 ). Computational and inferential difficulties with mixture posterior distributions . Journal of the American Statistical Association 95 : 957 – 970 .
   Web of Science ®Google Scholar
• Chib , S. ( 1996 ). Calculating posterior distributions and modal estimates in Markov mixture models . Journal of Econometrics 75 : 79 – 97 .
   Web of Science ®Google Scholar
• Dellaportas , P. , Forster , J. J. , Ntzoufras , I. ( 2000 ). Bayesian variable selection using the Gibbs sampler . In: Dey , D. K. , Ghosh , S. K. , Mallick , B. K. , eds. Generalized Linear Models: A Bayesian Perspective . New York : Marcel Dekker , pp. 273 – 286 .
   Google Scholar
• Denison , D. G. T. , Mallick , B. K. , Smith , A. F. M. ( 1998 ). Automatic Bayesian curve fitting . Journal of the Royal Statistical Society, Series B 60 : 333 – 350 .
   Google Scholar
• Elliott , R. J. , Aggoun , L. , Moore , J. B. ( 1995 ). Hidden Markov Models: Estimation and Control . New York : Springer .
   Google Scholar
• Franses , P. H. , van Dijk , D. ( 2000 ). Nonlinear Time Series Models in Empirical Finance . Cambridge : Cambridge University Press .
   Google Scholar
• Frühwirth-Schnatter , S. ( 2001 ). Markov Chain Monte Carlo estimation of classical and dynamic switching and mixture models . Journal of the American Statistical Association 96 : 194 – 209 .
   Web of Science ®Google Scholar
• Gamerman , D. ( 1997 ). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference . London : Chapman & Hall .
   Google Scholar
• George , E. I. , McCullogh , R. E. ( 1993 ). Variables selection via Gibbs-sampling . Journal of the American Statistical Association 88 : 881 – 889 .
   Web of Science ®Google Scholar
• George , E. I. , McCullogh , R. E. , Tsay , R. ( 1996 ). Two approaches to Bayesian model selection with applications . In: Berry , D. A. , Chaloner , K. M. , Geweke , J. K. , eds. Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner . New York : John Wiley & Sons , pp. 339 – 348 .
   Google Scholar
• Gilks , W. R. , Roberts , G. O. ( 1996 ). Strategies for improving MCMC . In: Gilks , W. R. , Richardson , S. , Spieghelhalter , D. J. , eds. Markov Chain Monte Carlo in Prectice . London : Chapman & Hall , pp. 89 – 114 .
   Google Scholar
• Hamilton , J. D. ( 1993 ). Estimation, inference and forecasting of time series subject to changes in regime . In: Maddala , G. S. , Rao , C. R. , Vinod , H. D. , eds. Handbook of Statistics , Vol. 11 . Amsterdam : North-Holland , pp. 231 – 259 .
   Google Scholar
• Kim , C. J. , Nelson , C. R. ( 1999 ). State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications . Cambridge : The MIT Press .
   Google Scholar
• Kohn , R. , Smith , M. , Chan , D. ( 2001 ). Nonparametric regression using linear combinations of basis functions . Statistics and Computing 11 : 313 – 322 .
   Web of Science ®Google Scholar
• Koski , T. ( 2001 ). Hidden Markov Models for Bioinformatics . Dordrecht : Kluwer Academic Publishers .
   Google Scholar
• Krolzig , H.-M. (1997). Markov-Switching Vector Autoregressions: Modelling, Statistical Inference and Applications to Business Cycle Analysis . Berlin : Springer.
   Google Scholar
• Kuo , L. , Mallick , B. ( 1998 ). Variable selection for regression models . Sankhya Series B 60 : 65 – 81 .
   Google Scholar
• Liu , J. S. , Wong , W. H. , Kong , A. ( 1994 ). Covariance structure of the Gibbs sampler with applications to the comparisons of the estimators and augmentation scheme . Biometrika 81 : 27 – 40 .
   Web of Science ®Google Scholar
• MacDonald , I. L. , Zucchini , W. ( 1997 ). Hidden Markov and Other Models for Discrete-Valued Time Series . London : Chapman & Hall .
   Google Scholar
• Mariott , J. , Ravishanker , N. , Gelfand , A. , Pai , J. ( 1996 ). Bayesian analysis of ARMA processes: Complete sampling-based inference under exact likelihoods . In: Berry , D. A. , Chaloner , K. M. , Geweke , J. K. , eds. Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner . New York : John Wiley & Sons , pp. 243 – 256 .
   Google Scholar
• Nott , D. J. , Green , P. J. ( 2004 ). Bayesian variable selection and the Swendsen-Wang algorithm . Journal of Computational and Graphical Statistics 13 : 1 – 17 .
   Google Scholar
• Nott , D. J. , Kohn , R. ( 2005 ). Adaptive sampling for Bayesian variable selection . Biometrika 92 : 747 – 763 .
   Web of Science ®Google Scholar
• Ntzoufras , I. , Forster , J. J. , Dellaportas , P. ( 2000 ). Stochastic search variable selection for log-linear models . Journal of Statistical Computation and Simulation 68 : 23 – 37 .
   Web of Science ®Google Scholar
• Paroli , R. , Spezia , L. ( 2007 ). Bayesian inference in non-homogeneous Markov mixtures of periodic autoregressions with state-dependent exogenous variables . Computational Statistics and Data Analysis doi:10.1016/j.csda.2007.09.020 .
   Google Scholar
• Schneider , U. , Corcoran , J. N. ( 2004 ). Perfect sampling for Bayesian variable selection in a linear regression model . Journal of Statistical Planning and Inference 126 : 153 – 171 .
   Google Scholar