The University of Southampton
University of Southampton Institutional Repository

Reversible jump methods for generalised linear models and generalised linear mixed models

Reversible jump methods for generalised linear models and generalised linear mixed models
Reversible jump methods for generalised linear models and generalised linear mixed models
A reversible jump algorithm for Bayesian model determination among generalised linear models, under relatively diffuse prior distributions for the model parameters, is proposed. Orthogonal projections of the current linear predictor are used so that knowledge from the current model parameters is used to make effective proposals. This idea is generalised to moves of a reversible jump algorithm for model determination among generalised linear mixed models. Therefore, this algorithm exploits the full flexibility available in the reversible jump method. The algorithm is demonstrated via two examples and compared to existing methods.
0960-3174
107-120
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Gill, Roger C.
584a7017-377f-4650-93b0-ab8361e98495
Overstall, Antony
c1d6c8bd-1c5f-49ee-a845-ec9ec7b20910
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Gill, Roger C.
584a7017-377f-4650-93b0-ab8361e98495
Overstall, Antony
c1d6c8bd-1c5f-49ee-a845-ec9ec7b20910

Forster, Jonathan J., Gill, Roger C. and Overstall, Antony (2012) Reversible jump methods for generalised linear models and generalised linear mixed models. Statistics and Computing, 22 (1), 107-120. (doi:10.1007/s11222-010-9210-3).

Record type: Article

Abstract

A reversible jump algorithm for Bayesian model determination among generalised linear models, under relatively diffuse prior distributions for the model parameters, is proposed. Orthogonal projections of the current linear predictor are used so that knowledge from the current model parameters is used to make effective proposals. This idea is generalised to moves of a reversible jump algorithm for model determination among generalised linear mixed models. Therefore, this algorithm exploits the full flexibility available in the reversible jump method. The algorithm is demonstrated via two examples and compared to existing methods.

Full text not available from this repository.

More information

e-pub ahead of print date: 28 October 2010
Published date: January 2012
Organisations: Statistics, Southampton Statistical Research Inst., Mathematics

Identifiers

Local EPrints ID: 186567
URI: https://eprints.soton.ac.uk/id/eprint/186567
ISSN: 0960-3174
PURE UUID: 79f57e58-769f-460a-ad75-609748360a38
ORCID for Jonathan J. Forster: ORCID iD orcid.org/0000-0002-7867-3411

Catalogue record

Date deposited: 13 May 2011 11:13
Last modified: 20 Jul 2019 01:20

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×