Optimum Experimental Designs (Oxford Statistical Science Series)
“Optimum Experimental Designs” by A. C. Atkinson, published by Clarendon Press on November 26, 1992, is a comprehensive resource that delves into the fundamentals of optimum experimental design theory. Spanning 352 pages, this book emphasizes the importance of statistical methods in the design and implementation of experiments, particularly in the presence of random error. It introduces key concepts such as models, least squares fitting, and optimum experimental designs, providing a solid foundation for readers interested in the intersection of experimental design and mathematical optimization.
Readers will find a structured exploration of both the theoretical and practical aspects of designing experiments. The book is divided into two main parts: the first discusses the advantages of a statistical approach, while the second offers a detailed examination of optimum design theory and various criteria for experiment design. Each chapter is self-contained and illustrated with examples from science and engineering, making it accessible to those with little prior statistical knowledge. This edition serves as a valuable guide for anyone involved in creating practical, efficient, and accurate experimental designs.
Official synopsis Publisher
A well-designed experiment is an efficient method for learning about the physical world, however since experiments in any setting cannot avoid random error, statistical methods are essential for their design and implementation, and for the analysis of results. In this book, the fundamentals of optimum experimental design theory are presented. In the first part, the advantages of a statistical approach to the design of experiments are discussed, and the ideas of models, least squares fitting, and optimum experimental designs are introduced. The second part presents a more detailed discussion of the general theory of optimum design and an evaluation of various criteria that may be appropriate for designing experiments. Specific experiments are detailed and algorithms for the construction of designs are given. Each chapter is a self-contained, illustrated with examples drawn from science and engineering. Little previous statistical knowledge is assumed, and the derivation of mathematical results has been avoided. This book should be of interest to everyone concerned with designing experiments that are practical, efficient, and accurate.
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