Model selection in random effects models for directed graphs using approximated Bayes factors

Journal article

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. © VVS, 2005.

Authors: Zijlstra, B; van Duijn, MAJ; Snijders, T

• Publication status: Published
• Journal: STATISTICA NEERLANDICA
• Volume: 59
• Issue: 1
• Pages: 107-118
• Publication date: 2005-02-01
• DOI: 10.1111/j.1467-9574.2005.00283.x
• EISSN: 1467-9574
• ISSN: 0039-0402
• Language: English
• Keywords: random effects; MCMC estimation; social network analysis; p(2) model