Stochastic search variable selection for log-linear models

被引：15
|
作者：
Ntzoufras, I
Forster, JJ [1 ]
Dellaportas, P
机构：
[1] Athens Univ Econ & Business, Dept Stat, Athens, Greece
[2] Univ Southampton, Dept Math, Southampton SO9 5NH, Hants, England
来源：
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2000年 / 68卷 / 01期
关键词：
Bayesian analysis; contingency table; Gibbs sampling; Markov chain Monte Carlo;
D O I：
10.1080/00949650008812054
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
We develop a Markov chain Monte Carlo algorithm, based on 'stochastic search variable selection' (George and McCulloch, 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.
引用
收藏
页码：23 / 37
页数：15
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