Model-Oriented Design of Experiments

This book presents ideas to stimulate the interest of the statistical community in the optimal design of experiments. This new edition now includes sections on design techniques based on the elemental Fisher information matrices (as opposed to Pearson information/moment matrices), allowing a seamless extension of the design techniques to inferential problems where the shape of distributions is essential for optimal design construction. Topics include designs for nonlinear models, models with random parameters and models with correlated observations, designs for model discrimination and misspecified (contaminated) models, and designs in functional spaces.

The authors avoid technical details, assuming a moderate background in calculus, matrix algebra, and statistics. In many places, however, hints are given as to how the basic ideas presented can be extended and elaborated to be applied in real scientific research and practical engineering problems.