Default Bayesian model determination methods for generalised linear mixed models

Overstall, A. M. and Forster, J. J. (2010) Default Bayesian model determination methods for generalised linear mixed models. Computational Statistics and Data Analysis, 54(12), pp. 3269-3288. (doi: 10.1016/j.csda.2010.03.008)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Overstall, Dr Antony
Authors: Overstall, A. M., and Forster, J. J.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Computational Statistics and Data Analysis
Publisher:Elsevier B.V.
ISSN (Online):1872-7352

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