Default Bayesian model determination methods for generalised linear mixed models


Overstall, Anthony M. and Forster, Jonathan J. (2009) Default Bayesian model determination methods for generalised linear mixed models. Southampton, UK, Southampton Statistical Sceinces Research Institute, 25pp. (S3RI Methodology Working Papers, (M09/01) ). (Submitted)

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Description/Abstract

In this paper, we consider a default strategy for fully Bayesian model determination for GLMMs. We address the two key issues of default prior specification and computation. In particular, we extend a concept of unit information to the priors for the parameters of a GLMM. We rely on a combination of MCMC and Laplace approximations to compute approximations to the posterior model probabilities and then further approximate these posterior model probabilities using bridge sampling. We apply our strategy to two examples.

Item Type: Monograph (Working Paper)
Keywords: unit information priors, bridge sampling, mcmc, laplace approximation
Subjects: H Social Sciences > HA Statistics
Divisions: University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
ePrint ID: 64862
Date Deposited: 20 Jan 2009
Last Modified: 27 Mar 2014 18:46
URI: http://eprints.soton.ac.uk/id/eprint/64862

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