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Bayesian variable and link determination for generalised linear models

Record type: Article

In this paper, we describe full Bayesian inference for generalised linear models where uncertainty exists about the structure of the linear predictor, the linear parameters and the link function. Choice of suitable prior distributions is discussed in detail and we propose an efficient reversible jump Markov chain Monte-Carlo algorithm for calculating posterior summaries. We illustrate our method with two data examples.

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Citation

Ntzoufras, I., Dellaportas, P. and Forster, J.J. (2003) Bayesian variable and link determination for generalised linear models Journal of Statistical Planning and Inference, 111, (1-2), pp. 165-180. (doi:10.1016/S0378-3758(02)00298-7).

More information

Published date: 2003
Keywords: logistic regression, markov chain monte-carlo, reversible jump
Organisations: Statistics

Identifiers

Local EPrints ID: 29968
URI: http://eprints.soton.ac.uk/id/eprint/29968
ISSN: 0378-3758
PURE UUID: c17289a5-e802-4612-b89f-ac1252940280

Catalogue record

Date deposited: 12 May 2006
Last modified: 17 Jul 2017 15:56

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