Springer, 2022. — 357 p. — ISBN 3031081757.
This book is designed as a
textbook for graduate students and as a resource for researchers seeking a thorough
mathematical treatment of its subject. It develops the main results of
regression and the analysis of variance, as well as the central results on
confounded and fractional factorial experiments. Matrix theory is
deemphasized; its role is taken instead by the theory of
linear transformations between vector spaces. The text gives a carefully paced and unified presentation of two topics,
linear models and experimental design. Students are assumed to have a solid background in linear algebra, basic knowledge of regression and analysis of variance, and some exposure to experimental design, and should be
comfortable with reading and constructing mathematical proofs. The book leads students into the
mathematical theory, including many examples both for motivation and for illustration. Over
130 exercises of varying difficulty are included. An extensive mathematical appendix and a detailed index make the text especially accessible.
Linear Models and Design can serve as a textbook for a year-long course in the topics covered, or for a one-semester course in either linear model theory or experimental design. It prepares students for more advanced topics in the field, and assists in developing a thoughtful approach to the existing literature. It includes a guide to terminology as well as discussion of the history and development of ideas, and offers a fresh perspective on the fundamental concepts and results of the subject.
Linear Models.
Effects in a Factorial Experiment.
Estimation.
Testing.
Multifactor Designs.
Fractional Factorial Designs.
Mathematical Background.
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