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Bayesian model choice based on Monte Carlo estimates of posterior model probabilities

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  • Congdon, Peter

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  • Congdon, Peter, 2006. "Bayesian model choice based on Monte Carlo estimates of posterior model probabilities," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 346-357, January.
    • Handle: RePEc:eee:csdana:v:50:y:2006:i:2:p:346-357
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    References listed on IDEAS

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    1. Kadane, Joseph B. & Lazar, Nicole A., 2004. "Methods and Criteria for Model Selection," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 279-290, January.
    2. Song, Xin-Yuan & Lee, Sik-Yum, 2002. "A Bayesian model selection method with applications," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 539-557, September.
    3. Scott S. L., 2002. "Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 337-351, March.
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    Cited by:

    1. Gelman Andrew & Robert Christian P. & Rousseau Judith, 2013. "Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin," Statistics & Risk Modeling, De Gruyter, vol. 30(2), pages 105-120, June.
    2. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
    3. So, Mike K.P. & Chan, Raymond K.S., 2014. "Bayesian analysis of tail asymmetry based on a threshold extreme value model," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 568-587.
    4. Chkrebtii, Oksana A. & Cameron, Erin K. & Campbell, David A. & Bayne, Erin M., 2015. "Transdimensional approximate Bayesian computation for inference on invasive species models with latent variables of unknown dimension," Computational Statistics & Data Analysis, Elsevier, vol. 86(C), pages 97-110.
    5. Cathy W. S. Chen & Richard H. Gerlach & Ann M. H. Lin, 2011. "Multi-regime nonlinear capital asset pricing models," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1421-1438, April.
    6. Aßmann, Christian & Boysen-Hogrefe, Jens, 2011. "A Bayesian approach to model-based clustering for binary panel probit models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 261-279, January.
    7. Gareth W. Peters & Pavel V. Shevchenko & Mario V. Wuthrich, 2009. "Model uncertainty in claims reserving within Tweedie's compound Poisson models," Papers 0904.1483, arXiv.org.
    8. Cathy Chen & Feng-Chi Liu & Mike So, 2013. "Threshold variable selection of asymmetric stochastic volatility models," Computational Statistics, Springer, vol. 28(6), pages 2415-2447, December.
    9. Cathy W. S. Chen & Richard H. Gerlach & Ann M. H. Lin, 2010. "Falling and explosive, dormant, and rising markets via multiple‐regime financial time series models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(1), pages 28-49, January.
    10. Rufo, M.J. & Martín, J. & Pérez, C.J., 2010. "New approaches to compute Bayes factor in finite mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3324-3335, December.
    11. Zongyuan Xia & Bo Tang & Long Qin & Huiguo Zhang & Xijian Hu, 2023. "Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China," Mathematics, MDPI, vol. 11(19), pages 1-15, October.
    12. Koulis Theodoro & Muthukumarana Saman & Briercliffe Creagh Dyson, 2014. "A Bayesian stochastic model for batting performance evaluation in one-day cricket," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(1), pages 1-13, January.
    13. Christian P. Robert, 2013. "Bayesian Computational Tools," Working Papers 2013-45, Center for Research in Economics and Statistics.

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