Bayesian variable and link determination for generalised linear models
Bayesian variable and link determination for generalised linear models
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.
logistic regression, markov chain monte-carlo, reversible jump
165-180
Ntzoufras, I.
3831ee6a-fc80-4ace-8dc6-a2c9be17769e
Dellaportas, P.
9f6d08e0-a1e3-4f8c-83e2-c69a1f2af0a3
Forster, J.J.
e3c534ad-fa69-42f5-b67b-11617bc84879
2003
Ntzoufras, I.
3831ee6a-fc80-4ace-8dc6-a2c9be17769e
Dellaportas, P.
9f6d08e0-a1e3-4f8c-83e2-c69a1f2af0a3
Forster, J.J.
e3c534ad-fa69-42f5-b67b-11617bc84879
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), .
(doi:10.1016/S0378-3758(02)00298-7).
Abstract
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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Published date: 2003
Keywords:
logistic regression, markov chain monte-carlo, reversible jump
Organisations:
Statistics
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Local EPrints ID: 29968
URI: http://eprints.soton.ac.uk/id/eprint/29968
ISSN: 0378-3758
PURE UUID: c17289a5-e802-4612-b89f-ac1252940280
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Date deposited: 12 May 2006
Last modified: 16 Mar 2024 02:45
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Author:
I. Ntzoufras
Author:
P. Dellaportas
Author:
J.J. Forster
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