Readership: Students and researchers in statistics, and experimentalists in the medical, pharmaceutical and chemical industries.
Anthony Atkinson, London School of Economics, Alexander Donev, School of Mathematics University of Manchester, and Randall Tobias, SAS Institute Inc.
"...the book will be a very valuable aid for all researchers and students who have to deal with, or are interested in, the efficient design of statistical experiments ... [it offers] a very apt blend of theoretical development and practical examples ... It is the view towards applications and design calculations which makes this book unique." - Friedrich Pukelsheim, International Statistical Review, August 2007
Preface I Background 1: Introduction 2: Some key ideas 3: Experimental strategies 4: The choice of a model 5: Models and least squares 6: Criteria for a good experiment 7: Standard designs 8: The analysis of experiments II Theory and applications 9: Optimum design theory 10: Criteria of optimality 11: D-optimum designs 12: Algorithms for the construction of exact D-optimum designs 13: Optimum experimental design with SAS 14: Experiments with both qualitative and quantitative factors 15: Blocking response surface designs 16: Mixture experiments 17: Nonlinear models 18: Bayesian optimum designs 19: Design augmentation 20: Model checking and designs for discriminating between models 21: Compound design criteria 22: Generalized linear models 23: Response transformation and structured variances 24: Time-dependent models with correlated observations 25: Further topics 26: Exercises Bibliography Author index Subject index
