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A novel reversible jump algorithm for generalized linear models

Author

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  • M. Papathomas
  • P. Dellaportas
  • V. G. S. Vasdekis

Abstract

We propose a novel methodology to construct proposal densities in reversible jump algorithms that obtain samples from parameter subspaces of competing generalized linear models with differing dimensions. The derived proposal densities are not restricted to moves between nested models and are applicable even to models that share no common parameters. We illustrate our methodology on competing logistic regression and log-linear graphical models, demonstrating how our suggested proposal densities, together with the resulting freedom to propose moves between any models, improve the performance of the reversible jump algorithm. Copyright 2011, Oxford University Press.

Suggested Citation

  • M. Papathomas & P. Dellaportas & V. G. S. Vasdekis, 2011. "A novel reversible jump algorithm for generalized linear models," Biometrika, Biometrika Trust, vol. 98(1), pages 231-236.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:1:p:231-236
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    File URL: http://hdl.handle.net/10.1093/biomet/asq071
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    Cited by:

    1. Oedekoven, C.S. & King, R. & Buckland, S.T. & Mackenzie, M.L. & Evans, K.O. & Burger, L.W., 2016. "Using hierarchical centering to facilitate a reversible jump MCMC algorithm for random effects models," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 79-90.
    2. Michail Papathomas, 2018. "On the correspondence from Bayesian log-linear modelling to logistic regression modelling with g-priors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 197-220, March.

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