Default Bayesian model determination for generalised liner mixed models
Default Bayesian model determination for generalised liner mixed models
In this thesis, an automatic, default, fully Bayesian model determination strategy for GLMMs is considered. This strategy must address the two key issues of default prior specification and computation.
Default prior distributions for the model parameters, that are based on a unit information concept, are proposed.
A two-phase computational strategy, that uses a reversible jump algorithm and implementation of bridge sampling, is also proposed.
This strategy is applied to four examples throughout this thesis.
Overstall, Anthony Marshall
4e234fd0-5cc9-467f-a9cf-2137861cd3af
9 March 2010
Overstall, Anthony Marshall
4e234fd0-5cc9-467f-a9cf-2137861cd3af
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Overstall, Anthony Marshall
(2010)
Default Bayesian model determination for generalised liner mixed models.
University of Southampton, School of Mathematics, Doctoral Thesis, 145pp.
Record type:
Thesis
(Doctoral)
Abstract
In this thesis, an automatic, default, fully Bayesian model determination strategy for GLMMs is considered. This strategy must address the two key issues of default prior specification and computation.
Default prior distributions for the model parameters, that are based on a unit information concept, are proposed.
A two-phase computational strategy, that uses a reversible jump algorithm and implementation of bridge sampling, is also proposed.
This strategy is applied to four examples throughout this thesis.
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Published date: 9 March 2010
Organisations:
University of Southampton, Faculty of Social, Human and Mathematical Sciences
Identifiers
Local EPrints ID: 170229
URI: http://eprints.soton.ac.uk/id/eprint/170229
PURE UUID: 491892fd-acae-4b79-b3ee-5bdc838de7aa
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Date deposited: 18 Jan 2011 14:54
Last modified: 14 Mar 2024 02:37
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Contributors
Author:
Anthony Marshall Overstall
Thesis advisor:
Jonathan J. Forster
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