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Deviance Information Criteria for Missing Data Models

Author

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  • Gilles Celeux

    (Crest)

  • Florence Forbes

    (Crest)

  • Christian P, Robert

    (Crest)

  • Michael Titterington

    (Crest)

Abstract

The deviance information criterion (DIC) introduced by Spiegelhalteret al. (2002) is directly inspired by linear and generalised linear models,but it is not so naturally de ned for missing data models. In this paper,we reassess the criterion for such models, testing the behaviour of variousextensions in the cases of mixture and random e ect models.

Suggested Citation

  • Gilles Celeux & Florence Forbes & Christian P, Robert & Michael Titterington, 2003. "Deviance Information Criteria for Missing Data Models," Working Papers 2003-30, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2003-30
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    References listed on IDEAS

    as
    1. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    2. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
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