Open Access
May 2013 Model Selection in Linear Mixed Models
Samuel Müller, J. L. Scealy, A. H. Welsh
Statist. Sci. 28(2): 135-167 (May 2013). DOI: 10.1214/12-STS410


Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model selection, which often aims to choose a parsimonious model with other desirable properties from a possibly very large set of candidate statistical models. Over the last 5–10 years the literature on model selection in linear mixed models has grown extremely rapidly. The problem is much more complicated than in linear regression because selection on the covariance structure is not straightforward due to computational issues and boundary problems arising from positive semidefinite constraints on covariance matrices. To obtain a better understanding of the available methods, their properties and the relationships between them, we review a large body of literature on linear mixed model selection. We arrange, implement, discuss and compare model selection methods based on four major approaches: information criteria such as AIC or BIC, shrinkage methods based on penalized loss functions such as LASSO, the Fence procedure and Bayesian techniques.


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Samuel Müller. J. L. Scealy. A. H. Welsh. "Model Selection in Linear Mixed Models." Statist. Sci. 28 (2) 135 - 167, May 2013.


Published: May 2013
First available in Project Euclid: 21 May 2013

zbMATH: 1331.62364
MathSciNet: MR3112403
Digital Object Identifier: 10.1214/12-STS410

Keywords: AIC , Bayes factor , BIC , Cholesky decomposition , fence , information criteria , Lasso , linear mixed model , Model selection , shrinkage methods

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.28 • No. 2 • May 2013
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