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 Sciences Research Institute 25pp. (S3RI Methodology Working Papers, (doi:10.1016/j.csda.2010.03.008) , M09/01).

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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)
Digital Object Identifier (DOI): doi:10.1016/j.csda.2010.03.008
Keywords: unit information priors, bridge sampling, mcmc, laplace approximation
Subjects:
ePrint ID: 64862
Date :
Date Event
19 January 2009Submitted
Date Deposited: 20 Jan 2009
Last Modified: 16 Apr 2017 17:18
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/64862

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