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Model-Oriented Design of Experiments
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This book's main goal is to present the basic ideas of statistical methods in design of optimal experiments, and to stimulate the interest of the statistical community in this area. 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 given as to how the ideas presented in this book can be extended and elaborated for use in real scientific research and practical engineering problems.
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