Model selection in linear mixed-effect models

Simona Buscemi () and Antonella Plaia ()
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Simona Buscemi: University of Palermo
Antonella Plaia: University of Palermo

AStA Advances in Statistical Analysis, 2020, vol. 104, issue 4, No 1, 529-575

Abstract: Abstract Linear mixed-effects models are a class of models widely used for analyzing different types of data: longitudinal, clustered and panel data. Many fields, in which a statistical methodology is required, involve the employment of linear mixed models, such as biology, chemistry, medicine, finance and so forth. One of the most important processes, in a statistical analysis, is given by model selection. Hence, since there are a large number of linear mixed model selection procedures available in the literature, a pressing issue is how to identify the best approach to adopt in a specific case. We outline mainly all approaches focusing on the part of the model subject to selection (fixed and/or random), the dimensionality of models and the structure of variance and covariance matrices, and also, wherever possible, the existence of an implemented application of the methodologies set out.

Keywords: Linear mixed model; Mixed model selection; AIC; BIC; MCP; LASSO; Shrinkage methods; MDL (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10182-019-00359-z

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