Here, the authors explain the basic ideas so as to generate interest in modern problems of experimental design. The topics discussed include designs for inference based on nonlinear models, designs for models with random parameters and stochastic processes, designs for model discrimination and incorrectly specified (contaminated) models, as well as examples of designs in functional spaces. Since the authors avoid technical details, the book assumes only a moderate background in calculus, matrix algebra, and statistics. However, at many places, hints are given as to how readers may enhance and adopt the basic ideas for advanced problems or applications. This allows the book to be used for courses at different levels, as well as serving as a useful reference for graduate students and researchers in statistics and engineering.

About the Author:

Valerii V. Fedorov is Senior Reseacher at Oak Ridge National Laboratory and author of "The Theory of Optimal Experiments." Peter Hackl is Professor of Statistics at the University of Economics and Business Administration in Vienna, Austria, and President of the Austrian Statistical Society.

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