Abstract
Linear mixed models (Laird and Ware, 1982) and generalized linear mixed models (GLMMs) (Breslow and Clayton, 1993) have been widely used in many research areas, especially in the area of biomedical research, to analyze longitudinal and clustered data and multiple outcome data. In a mixed effects model, subject-specific random effects are used to explicitly model between-subject variation in the data and often assumed to follow a mean zero parametric distribution, e.g., multivariate normal, that depends on some unknown variance components. A large literature was developed in the last two decades for the estimation of regression coefficients and variance components in mixed effects models. See Diggle et al. (2002) and Verbeke and Molenberghs (2000, 2005) for an overview.
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Zhang, D., Lin, X. (2008). Variance Component Testing in Generalized Linear Mixed Models for Longitudinal/Clustered Data and other Related Topics. In: Dunson, D.B. (eds) Random Effect and Latent Variable Model Selection. Lecture Notes in Statistics, vol 192. Springer, New York, NY. https://doi.org/10.1007/978-0-387-76721-5_2
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