Description
This book presents the basic ideas of statistical methods in the design of optimal 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, suggestions are made as to how the ideas presented in this book can be extended and elaborated for use in real scientific research and practical engineering problems.
Table of Contents
Preface.- Introduction.- 1. Some Facts From Regression Analysis.- 2. Convex Design Theory.- 3. Numerical Techniques.- 4. Optimal Design under Constraints.- 5. Special Cases and Applications.- A. Elemental Fisher Information.- B. Selected Formulas from Matrix Theory.- C. List of Symbols.- References.- Index.
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