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Model selection in random effects models for directed graphs using approximated Bayes factors
Author(s) -
Zijlstra Bonne J. H.,
Duijn Marijtje A. J.,
Snijders Tom A. B.
Publication year - 2005
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2005.00283.x
Subject(s) - bayes' theorem , selection (genetic algorithm) , bayes factor , bayesian network , model selection , bayesian programming , mathematics , computer science , bayesian probability , statistics , econometrics , machine learning
With the development of an MCMC algorithm, Bayesian model selection for the p 2 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.