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Default Bayesian model determination for generalised liner mixed models

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
Overstall, Anthony Marshall
4e234fd0-5cc9-467f-a9cf-2137861cd3af
Forster, Jonathan
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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More information

Published date: 9 March 2010
Organisations: University of Southampton, Faculty of Social, Human and Mathematical Sciences

Identifiers

Local EPrints ID: 170229
URI: https://eprints.soton.ac.uk/id/eprint/170229
PURE UUID: 491892fd-acae-4b79-b3ee-5bdc838de7aa
ORCID for Jonathan Forster: ORCID iD orcid.org/0000-0002-7867-3411

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Date deposited: 18 Jan 2011 14:54
Last modified: 24 May 2019 00:38

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