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.

线性混合效应模型是一类模型，广泛用于分析不同类型的数据：纵向，聚类和面板数据。需要统计方法的许多领域都涉及线性混合模型的使用，例如生物学，化学，医学，金融等。在统计分析中，最重要的过程之一是模型选择。因此，由于文献中提供了大量的线性混合模型选择程序，因此紧迫的问题是如何确定在特定情况下采用的最佳方法。我们主要概述了所有方法，重点关注模型中要选择的部分（固定和/或随机），模型的维数以及方差和协方差矩阵的结构，并且在可能的情况下，