Electronic Journal of Statistics

A note on BIC in mixed-effects models

Maud Delattre, Marc Lavielle, and Marie-Anne Poursat

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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.

Article information

Source
Electron. J. Statist., Volume 8, Number 1 (2014), 456-475.

Dates
First available in Project Euclid: 2 May 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1399035845

Digital Object Identifier
doi:10.1214/14-EJS890

Mathematical Reviews number (MathSciNet)
MR3200764

Zentralblatt MATH identifier
1348.62186

Subjects
Primary: 62J02: General nonlinear regression 62J12: Generalized linear models

Keywords
Bayesian Information Criterion BIC mixed effects model variable selection

Citation

Delattre, Maud; Lavielle, Marc; Poursat, Marie-Anne. A note on BIC in mixed-effects models. Electron. J. Statist. 8 (2014), no. 1, 456--475. doi:10.1214/14-EJS890. https://projecteuclid.org/euclid.ejs/1399035845


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