Journal of the American Statistical Association vol:91 issue:433 pages:217-221
This article investigates the impact of the normality assumption for random effects on their estimates in the linear mixed-effects model. II shows that if the distribution of random effects is a finite mixture of normal distributions, then the random effects may be badly estimated if normality is assumed, and the current methods for inspecting the appropriateness of the model assumptions are not sound. Further, it is argued that a better way to detect the components of the mixture is to build this assumption in the model and then ''compare'' the fitted model with the Gaussian model. All of this is illustrated on two practical examples.