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Deviance Information Criteria for Missing Data Models

Gilles Celeux 1 Florence Forbes 1 Christian Robert 1 Mike Titterington 1
1 IS2 - Statistical Inference for Industry and Health
Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
Abstract : The deviance information criterion (DIC) introduced by is directly inspired by linear and generalised linear models, but it is not so naturally defined for missing data models. In this paper, we reassess the criterion for such models, testing the behaviour of various extensions in the cases of mixture and random effect models.
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Submitted on : Tuesday, May 23, 2006 - 6:36:41 PM
Last modification on : Friday, February 4, 2022 - 3:24:27 AM
Long-term archiving on: : Sunday, April 4, 2010 - 10:35:11 PM


  • HAL Id : inria-00071724, version 1



Gilles Celeux, Florence Forbes, Christian Robert, Mike Titterington. Deviance Information Criteria for Missing Data Models. [Research Report] RR-4859, INRIA. 2003. ⟨inria-00071724⟩



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