Stochastic search variable selection for log-linear models

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).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1080/00949650008812054
ISSNs: 0094-9655 (print)
Related URLs:
Keywords: bayesian analysis, contingency table, gibbs sampling, markov chain monte carlo
Subjects: Q Science > QA Mathematics
H Social Sciences > HA Statistics
Divisions : University Structure - Pre August 2011 > School of Mathematics > Statistics
ePrint ID: 29961
Accepted Date and Publication Date:
Date Deposited: 12 Mar 2007
Last Modified: 31 Mar 2016 11:56

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