6533b837fe1ef96bd12a1ded
RESEARCH PRODUCT
Model selection in linear mixed-effect models
Antonella PlaiaSimona Buscemisubject
Statistics and ProbabilityMixed modelEconomics and EconometricsMathematical optimizationLinear mixed modelApplied MathematicsModel selectionMDLVariance (accounting)LASSOCovarianceGeneralized linear mixed modelMixed model selectionLasso (statistics)Shrinkage methodsModeling and SimulationMCPAICBICSettore SECS-S/01 - StatisticaSocial Sciences (miscellaneous)AnalysisSelection (genetic algorithm)Curse of dimensionalitydescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-10-28 |