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.
Lingua originale | English |
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pagine (da-a) | 529-575 |
Numero di pagine | 47 |
Rivista | AStA Advances in Statistical Analysis |
Volume | 104 |
Stato di pubblicazione | Published - 2020 |
All Science Journal Classification (ASJC) codes
- Analysis
- Statistics and Probability
- Modelling and Simulation
- Social Sciences (miscellaneous)
- Economics and Econometrics
- Applied Mathematics