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Stochastic search variable selection for log-linear models

Stochastic search variable selection for log-linear models
Stochastic search variable selection for log-linear models
We develop a Markov chain Monte Carlo algorithm, based on 'stochastic search variable selection' (George and McCuUoch, 1993), for identifying promising log-linear models. The method may be used in the analysis of multi-way contingency tables where the set of plausible models is very large.
bayesian analysis, contingency table, gibbs sampling, markov chain monte carlo
0094-9655
23-37
Ntzoufras, Ioannis
334f2302-b765-48d1-90b6-121ec8e31131
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd
Ntzoufras, Ioannis
334f2302-b765-48d1-90b6-121ec8e31131
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd

Ntzoufras, Ioannis, Forster, Jonathan J. and Dellaportas, Petros (2000) Stochastic search variable selection for log-linear models. Journal of Statistical Computation and Simulation, 68 (1), 23-37. (doi:10.1080/00949650008812054).

Record type: Article

Abstract

We develop a Markov chain Monte Carlo algorithm, based on 'stochastic search variable selection' (George and McCuUoch, 1993), for identifying promising log-linear models. The method may be used in the analysis of multi-way contingency tables where the set of plausible models is very large.

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Published date: 2000
Related URLs:
Keywords: bayesian analysis, contingency table, gibbs sampling, markov chain monte carlo
Organisations: Statistics

Identifiers

Local EPrints ID: 29961
URI: http://eprints.soton.ac.uk/id/eprint/29961
DOI: doi:10.1080/00949650008812054
ISSN: 0094-9655
PURE UUID: ad91a24d-7346-49f3-a930-8605f27652c4
ORCID for Jonathan J. Forster: ORCID iD orcid.org/0000-0002-7867-3411

Catalogue record

Date deposited: 12 Mar 2007
Last modified: 16 Mar 2024 02:45

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Contributors

Author: Ioannis Ntzoufras
Author: Jonathan J. Forster ORCID iD
Author: Petros Dellaportas

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