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 …

Considering model averaging estimation in generalized linear models, we propose a weight
choice criterion based on the Kullback–Leibler (KL) loss with a penalty term. This criterion is …

BACKGROUND: Since many health data are unavailable at the county level, policymakers
sometimes rely on state-level datasets to understand the health needs of their communities …

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 …

The application of generalized linear mixed models presents some major challenges for
both estimation, due to the intractable marginal likelihood, and model selection, as we …

We study model selection for clustered data, when the focus is on cluster specific inference.
Such data are often modelled using random effects, and conditional Akaike information was …

Large sample techniques are fundamental to all fields of statistics. Mixed effects models,
including linear mixed models, generalized linear mixed models, non-linear mixed effects …

With the introductory paragraph, we explain the fundamental concepts and basic notions of
this section. For you, the analyst, who has the difficult task to deal with measurements …

Inference in generalized linear models with crossed effects is often made cumbersome by
the high-dimensional intractable integrals involved in the likelihood function. We propose an …