Praise for the First Edition:" If you... want an up-to-date, definitive reference written by
authors who have contributed much to this field, then this book is an essential addition to …

This important book describes procedures for selecting a model from a large set of
competing statistical models. It includes model selection techniques for univariate and …

The method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different theories exist, as are …

The promise of big data is enormous and nowhere is it more critical than in its potential to
contain important, undiscovered interdependence among thousands of variables. Networks …

In principle, the Bayesian approach to model selection is straightforward. Prior probability
distributions are used to describe the uncertainty surrounding all unknowns. After observing …

Supersaturated designs are fractional factorial designs in which the run size (n) is too small
to estimate all the main effects. Under the effect sparsity assumption, the use of …

This text presents a unified treatment of experimental designs and design concepts
commonly used in practice. It connects the objectives of research to the type of experimental …

Kriging is a useful method for develo** metamodels for product design optimization. The
most popular kriging method, known as ordinary kriging, uses a constant mean in the model …

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

In ultrahigh-dimensional data analysis, it is extremely challenging to identify important
interaction effects, and a top concern in practice is computational feasibility. For a dataset …