Default Bayesian model determination methods for generalised linear mixed models
Default Bayesian model determination methods for generalised linear mixed models
A default strategy for fully Bayesian model determination for generalised linear mixed models (GLMMs) is considered which addresses the two key issues of default prior specification and computation. In particular, the concept of unit-information priors is extended to the parameters of a GLMM. A combination of Markov chain Monte Carlo (MCMC) and Laplace approximations is used to compute approximations to the posterior model probabilities to find a subset of models with high posterior model probability. Bridge sampling is then used on the models in this subset to approximate the posterior model probabilities more accurately. The strategy is applied to four examples.
3269-3288
Overstall, Antony
c1d6c8bd-1c5f-49ee-a845-ec9ec7b20910
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
1 December 2010
Overstall, Antony
c1d6c8bd-1c5f-49ee-a845-ec9ec7b20910
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Overstall, Antony and Forster, Jonathan J.
(2010)
Default Bayesian model determination methods for generalised linear mixed models.
Computational Statistics & Data Analysis, 54 (12), .
(doi:10.1016/j.csda.2010.03.008).
Abstract
A default strategy for fully Bayesian model determination for generalised linear mixed models (GLMMs) is considered which addresses the two key issues of default prior specification and computation. In particular, the concept of unit-information priors is extended to the parameters of a GLMM. A combination of Markov chain Monte Carlo (MCMC) and Laplace approximations is used to compute approximations to the posterior model probabilities to find a subset of models with high posterior model probability. Bridge sampling is then used on the models in this subset to approximate the posterior model probabilities more accurately. The strategy is applied to four examples.
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Published date: 1 December 2010
Organisations:
Statistics, Southampton Statistical Research Inst.
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Local EPrints ID: 151609
URI: http://eprints.soton.ac.uk/id/eprint/151609
ISSN: 0167-9473
PURE UUID: eb9ce046-abeb-475a-91f7-c0f28255100b
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Date deposited: 11 May 2010 16:19
Last modified: 14 Mar 2024 02:52
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Author:
Jonathan J. Forster
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