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.<br/><br/>Default prior distributions for the model parameters, that are based on a unit information concept, are proposed.<br/><br/>A two-phase computational strategy, that uses a reversible jump algorithm and implementation of bridge sampling, is also proposed.<br/><br/>This strategy is applied to four examples throughout this thesis.

Name dc:type.qualificationname

Ph.D.

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

University of Southampton

Year dc:date.issued

2010

Author dc:creator

• Overstall, Anthony Marshall

Advisor dc:contributor.advisor

• Forster, Jonathan J.