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Model choice using reversible jump Markov chain Monte Carlo

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  • David I. Hastie
  • Peter J. Green

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  • David I. Hastie & Peter J. Green, 2012. "Model choice using reversible jump Markov chain Monte Carlo," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 309-338, August.
    • Handle: RePEc:bla:stanee:v:66:y:2012:i:3:p:309-338
    DOI: j.1467-9574.2012.00516.x
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    References listed on IDEAS

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    1. S. P. Brooks & P. Giudici & G. O. Roberts, 2003. "Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 3-39, January.
    2. Al-Awadhi, Fahimah & Hurn, Merrilee & Jennison, Christopher, 2004. "Improving the acceptance rate of reversible jump MCMC proposals," Statistics & Probability Letters, Elsevier, vol. 69(2), pages 189-198, August.
    3. Francesco Bartolucci & Luisa Scaccia & Antonietta Mira, 2006. "Efficient Bayes factor estimation from the reversible jump output," Biometrika, Biometrika Trust, vol. 93(1), pages 41-52, March.
    4. repec:dau:papers:123456789/6072 is not listed on IDEAS
    5. repec:dau:papers:123456789/6040 is not listed on IDEAS
    6. Ricardo S. Ehlers & Stephen P. Brooks, 2008. "Adaptive Proposal Construction for Reversible Jump MCMC," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 677-690, December.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Jiang, Bin & Yang, Yanrong & Gao, Jiti & Hsiao, Cheng, 2021. "Recursive estimation in large panel data models: Theory and practice," Journal of Econometrics, Elsevier, vol. 224(2), pages 439-465.
    3. C. Berrett & B. Gurney & D. Arthur & T. Moon & G. P. Williams, 2023. "A Bayesian change point modeling approach to identify local temperature changes related to urbanization," Environmetrics, John Wiley & Sons, Ltd., vol. 34(3), May.
    4. A. Mohammadi & M. Salehi-Rad & E. Wit, 2013. "Using mixture of Gamma distributions for Bayesian analysis in an M/G/1 queue with optional second service," Computational Statistics, Springer, vol. 28(2), pages 683-700, April.
    5. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    6. Pandolfi, Silvia & Bartolucci, Francesco & Friel, Nial, 2014. "A generalized multiple-try version of the Reversible Jump algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 298-314.
    7. Ban Kheng Tan & Anastasios Panagiotelis & George Athanasopoulos, 2017. "Bayesian Inference for a 1-Factor Copula Model," Monash Econometrics and Business Statistics Working Papers 6/17, Monash University, Department of Econometrics and Business Statistics.
    8. Liu, Xiaochun & Luger, Richard, 2015. "Unfolded GARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 186-217.
    9. Gianluca Mastrantonio, 2022. "The modelling of movement of multiple animals that share behavioural features," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 932-950, August.

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