Over the last three decades, mixed models have been frequently used in a wide range of
small area applications. Such models offer great flexibilities in combining information from …

It has been an amazing time since the publication of the first edition, with vast changes
taking place in the fields of mixed effects models and their applications. At the time when the …

We generalize the well‐known R2 measure for linear regression to linear mixed effects
models. Our work was motivated by a cluster‐randomized study conducted by the Eastern …

Placing data in the context of the scientific discovery of knowledge through experimentation,
Practical Data Analysis for Designed Experiments examines issues of comparing groups …

The paper proposes a small area estimation approach that combines small area random
effects with a smooth, non-parametrically specified trend. By using penalized splines as the …

Residuals are frequently used to evaluate the validity of the assumptions of statistical
models and may also be employed as tools for model selection. For standard (normal) linear …

A quote from the preface of the first edition:“Large-sample techniques provide solutions to
many practical problems; they simplify our solutions to difficult, sometimes intractable …

Longitudinal studies often incur several problems that challenge standard statistical
methods for data analysis. These problems include non-ignorable missing data in …

In tropical and sub-tropical regions, biomass carbon (C) losses through forest degradation
are recognized as central to global terrestrial carbon cycles. Accurate estimation of forest …

The paper reviews the linear mixed model with a focus on parameter estimation and
inference. Parameter estimation for the different components of the model are reviewed, with …