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.
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
January 2012
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), .
(doi:10.1007/s11222-010-9210-3).
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.
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e-pub ahead of print date: 28 October 2010
Published date: January 2012
Organisations:
Statistics, Southampton Statistical Research Inst., Mathematics
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Local EPrints ID: 186567
URI: http://eprints.soton.ac.uk/id/eprint/186567
ISSN: 0960-3174
PURE UUID: 79f57e58-769f-460a-ad75-609748360a38
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Date deposited: 13 May 2011 11:13
Last modified: 15 Mar 2024 03:27
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Author:
Jonathan J. Forster
Author:
Roger C. Gill
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