Model selection in linear mixed-effect models

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Pages (from-to)529-575
Number of pages47
JournalAStA Advances in Statistical Analysis
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Analysis
  • Statistics and Probability
  • Modelling and Simulation
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Applied Mathematics


Dive into the research topics of 'Model selection in linear mixed-effect models'. Together they form a unique fingerprint.

Cite this