Many texts are excellent sources of knowledge about individual statistical tools, but the art of
data analysis is about choosing and using multiple tools. Instead of presenting isolated …

We consider hierarchical generalized linear models which allow extra error components in
the linear predictors of generalized linear models. The distribution of these components is …

This is the second edition of a monograph on generalized linear models with random effects
that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with …

This book reviews the state-of-the-art advances in skew-elliptical distributions and provides
many new developments in a single volume, collecting theoretical results and applications …

We review the current state of nonparametric Bayesian inference. The discussion follows a
list of important statistical inference problems, including density estimation, regression …

Generalized Linear Models: A Unified Approach provides an introduction to and overview of
GLMs, with each chapter carefully laying the groundwork for the next. The Second Edition …

This volume describes how to conceptualize, perform, and critique traditional generalized
linear models (GLMs) from a Bayesian perspective and how to use modern computational …

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain
modeling flexibility and robustness against mis-specification of the probability model. In the …

Bayesian nonparametric inference is a relatively young area of research and it has recently
undergone a strong development. Most of its success can be explained by the considerable …

In recent years, Bayesian nonparametric inference, both theoretical and computational, has
witnessed considerable advances. However, these advances have not received a full critical …