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American Journal of Mathematical and Management Sciences Volume 31, 2011 - Issue 1-2
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Original Articles

Predictive Bayesian Model Selection

Tomohiro AndoGraduate School of Business Administration, Keio University 2-1-1 Hiyoshi-Honcho, Kohoku-ku, Yokohama-shi, Kanagawa, 223-8523, Japan
Pages 13-38 | Published online: 06 Aug 2013

  • AitkinM. (1991). Posterior Bayes factors (with discussion). Journal of the Royal Statistical Society, B53, 111–128.
  • AkaikeH. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, AC–19, 716–723.
  • AkaikeH. (1991). Discussion on “Posterior Bayes factors”. Journal of the Royal Statistical Society B53, 135.
  • AndoT. (2007). Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models. Biometrika, 94, 443–458.
  • AndoT. (2010a). Bayesian statistical modeling and model selection. Chapman&Hall/CRC, London.
  • AndoT. (2010b). Bayesian portfolio selection under a multifactor asset return model with predictive Bayesian model selection. Proceeding of International Association for the Scientific Knowledge Global Management 2010, 111–120.
  • AndoT. and TsayR. (2010). Predictive marginal likelihood for the Bayesian model selection and averaging. International Journal of Forecasting, 26, 744–763.
  • AndoT., KonishiS. and ImotoS. (2008). Nonlinear regression modeling via regularized radial basis function networks. Journal of Statistical Planning and Inference, 138, 3616–3633.
  • Barndorff-NielsenO. E. and CoxD. R. (1989). Asymptotic Techniques for Use in Statistics, Chapman and Hall, London.
  • BergA., MeyerR. and YuJ. (2004). Deviance information criterion comparing stochastic volatility models. Journal of Business and Economic Statistics, 22, 107–120.
  • BergerJ. O. and PericchiL. R. (1996). The intrinsic Bayes factor for model selection and prediction. Journal of The American Statistical Association, 91, 109–122.
  • Celeux, ForbesG., RobertF.C. and TitteringtonD. M. (2006). Deviance information criteria for missing data models, Bayesian Analysis, 1, 651–674.
  • ChibS. (1995). Marginal Likelihood from the Gibbs Output. Journal of the American Statistical Association. 90, 1313–1321.
  • ChibS., NardariF. and ShephardN. (2002). Markov Chain Monte Carlo Methods for Stochastic Volatility Models. Journal of Econometrics, 108, 281–316.
  • EfronB. and TibshiraniR. J. (1993). An Introduction to the Bootstrap. Chapman and Hall, New York.
  • EilersP. H. C. and MarxB. D. (1996). Flexible smoothing with B-splines and penalties (with Discussion). Statistical Science, 11, 89–121.
  • GelfandA. E. and DeyD. K. (1994). Bayesian model choice: asymptotic and exact calculations. Journal of the Royal Statistical Society, B56, 501–514.
  • GelfandA. and GhoshS. (1998). Model choice: a minimum posterior predictive loss approach. Biometrika, 85, 1–11.
  • HurvichC. M. SimonoffJ. S. and TsaiC.-L. (1998), Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society, B60, 271–293.
  • KadaneJ. B. and LazarN. A. (2004). Methods and criteria for model selection. Journal of the American Statistical Association, 99, 279–290.
  • KassR. and RafteryA. (1995). Bayes factors and model uncertainty. Journal of the American Statistical Association, 90, 773–795.
  • KimS. ShephardN. and ChibS. (1998). Stochastic volatility: likelihood inference comparison with ARCH models. Review of Economic Studies, 65, 361–393.
  • KonishiS. and KitagawaG. (1996). Generalized information criteria in model selection. Biometrika, 83, 875–890.
  • KonishiS., AndoT. and ImotoS. (2004). Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika, 91, 27–43.
  • McCullaghP. and NeiderJ. A. (1989). Generalized Linear Models. Chapman and Hall, London.
  • MukhopadhyayaN., GhoshJ. K. and BergerJ. O. (2003). Some Bayesian predictive approaches to model selection. Statistics and Probability Letters, 73, 369–379.
  • MurataN., YoshizawaS. and AmariS. (1994). Network information criterion determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks, 5, 865–872.
  • NewtonM. A. and RafteryA. E. (1994). Approximate Bayesian inference with the weighted likelihood bootstrap (with Discussion). Journal of the Royal Statistical Society, B56, 3–48.
  • O'HaganA. (1995). Fractional Bayes factors for model comparison (with Discussion). Journal of the Royal Statistical Society, B57, 99–138.
  • PerezJ. M. and BergerJ. O. (2002). Expected-posterior prior distributions for model selection. Biometrika, 89, 491–512.
  • PlummerM. (2008). Penalized loss functions for Bayesian model comparison. Biostatistics, 9, 523–539.
  • Rahul, PandeyG. S. and SinghO. P. (2009). Population Projection of Kerala using Bayesian Methodology. Asian Journal of Applied Sciences, 2, 402–413.
  • RobertC. P. and TitteringtonD. M. (2002). Discussion on ‘Bayesian measures of model complexity and fit’. Journal of the Royal Statistical Society, B64, 621–622.
  • SinghO. P. PandeyG. S. and Rahul (2007). Population projection of India (2001-2051) using MCMC technique in Bayesian inference. American Journal of Mathematical and Management Sciences, 27, 139–152.
  • SpiegelhalterD. J., BestN. G., CarlinB. P. & van der LindeA. (2002). Bayesian measures of model complexity and fit (with Discussion). Journal of the Royal Statistical Society, B64, 583–639.
  • van der LindeA. (2005). DIC in variable selection. Statistica Neerlandica, 59, 45–56.
  • ZellnerA. and AndoT. (2010). A Direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model with informative prior (in Japanese). Journal of the Japan Statistical Society, 39, 297–312.

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