Model selection in random effects models for directed graphs using approximated Bayes factors
Article first published online: 11 MAR 2005
Volume 59, Issue 1, pages 107–118, February 2005
How to Cite
Zijlstra, B. J. H., van Duijn, M. A. J. and Snijders, T. A. B. (2005), Model selection in random effects models for directed graphs using approximated Bayes factors. Statistica Neerlandica, 59: 107–118. doi: 10.1111/j.1467-9574.2005.00283.x
- Issue published online: 11 MAR 2005
- Article first published online: 11 MAR 2005
- Received: June 2004. Revised: December 2004.
- p2 model;
- social network analysis;
- random effects;
- MCMC estimation
With the development of an MCMC algorithm, Bayesian model selection for the p2 model for directed graphs has become possible. This paper presents an empirical exploration in using approximate Bayes factors for model selection. For a social network of Dutch secondary school pupils from different ethnic backgrounds it is investigated whether pupils report that they receive more emotional support from within their own ethnic group. Approximated Bayes factors seem to work, but considerable margins of error have to be reckoned with.