Model-Oriented Design of Experiments

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.