Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models
There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed...[Show more]
|Collections||ANU Research Publications|
|Source:||International Statistical Review|
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