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A note on BIC in mixed-effects models

Abstract : The Bayesian Information Criterion (BIC) is widely used for variable selection in mixed effects models. However, its expression is unclear in typical situations of mixed effects models, where simple definition of the sample size is not meaningful.We derive an appropriate BIC expression that is consistent with the random effect structure of the mixed effects model. We illustrate the behavior of the proposed criterion through a simulation experiment and a case study and we recommend its use as an alternative to various existing BIC versions that are implemented in available software.
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Submitted on : Tuesday, March 3, 2015 - 12:53:04 PM
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Maud Delattre, Marc Lavielle, Marie-Anne Poursat. A note on BIC in mixed-effects models. Electronic journal of statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2014, 8, pp.456--475. ⟨10.1214/14-EJS890⟩. ⟨hal-00991708⟩



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