This is author-approved bcc: Optimal design of experiments is an essential component of any research that aims at the estimation of unknown parameters, at model validation, or the comparison and selection of the best among several competing models. The authors' goals are to explain the basic ideas and to create 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, and examples of designs in functional spaces. As 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 how the reader may enhance and adopt the basic ideas for advanced problems or applications. This will allow the book to be used for courses at different levels, and it will be a useful reference for graduate students and researchers in statistics and engineering. 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.

Table of Contents:
1 Some Facts From Regression Analysis.- 1.1 The Linear Model.- 1.2 More about the Information Matrix.- 1.3 Generalized Versions of the Linear Regression Model.- 1.4 Nonlinear Models.- 2 Convex Design Theory.- 2.1 Optimality Criteria.- 2.2 Some Properties of Optimality Criteria.- 2.3 Continuous Optimal Designs.- 2.4 The Sensitivity Function and Equivalence Theorems.- 2.5 Some Examples.- 2.6 Complements.- 3 Numerical Techniques.- 3.1 First Order Algorithm:D-criterion.- 3.2 First Order Algorithm: The General Case.- 3.3 Finite Sample Size.- 4 Optimal Design under Constraints.- 4.1 Cost Constraints.- 4.2 Constraints for Auxiliary Criteria.- 4.3 Directly Constrained Design Measures.- 5 Special Cases and Applications.- 5.1 Designs for Time-Dependent Models.- 5.2 Regression Models with Random Parameters.- 5.3 Mixed Models and Correlated Observations.- 5.4 Design for “Contaminated” Models.- 5.5 Model Discrimination.- 5.6 Nonlinear Regression.- 5.7 Design in Functional. Spaces.- A Some Results from Matrix Algebra.- B List of Symbols.- References.