Mixed-effects models have found broad applications in various
fields. As a result, the interest in learning and using these
models is rapidly growing. On the other hand, some of these models,
such as the linear mixed models and generalized linear mixed
models, are highly parametric, involving distributional assumptions
that may not be satisfied in real-life problems. Therefore, it is
important, from a practical standpoint, that the methods of
inference about these models are robust to violation of model
assumptions. Fortunately, there is a full scale of methods
currently available that are robust in certain aspects. Learning
about these methods is essential for the practice of mixed-effects
models.This research monograph provides a comprehensive account of
methods of mixed model analysis that are robust in various aspects,
such as to violation of model assumptions, or to outliers. It is
suitable as a reference book for a practitioner who uses the
mixed-effects models, and a researcher who studies these models. It
can also be treated as a graduate text for a course on
mixed-effects models and their applications.
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