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

Authors

  • Bonne J. H. Zijlstra,

    1. Department of Sociology/Statistics and Measurement Theory, Heijmans Institute/ICS, University of Groningen, Grote Rozenstraat 31, 9712 TG, Groningen, the Netherlands
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  • Marijtje A. J. van Duijn,

    1. Department of Sociology/Statistics and Measurement Theory, Heijmans Institute/ICS, University of Groningen, Grote Rozenstraat 31, 9712 TG, Groningen, the Netherlands
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  • Tom A. B. Snijders

    1. Department of Sociology/Statistics and Measurement Theory, Heijmans Institute/ICS, University of Groningen, Grote Rozenstraat 31, 9712 TG, Groningen, the Netherlands
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Abstract

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.

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