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Linear Model Theory - With Examples and Exercises (Paperback, 1st ed. 2020)
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Linear Model Theory - With Examples and Exercises (Paperback, 1st ed. 2020)
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This textbook presents a unified and rigorous approach to best
linear unbiased estimation and prediction of parameters and random
quantities in linear models, as well as other theory upon which
much of the statistical methodology associated with linear models
is based. The single most unique feature of the book is that each
major concept or result is illustrated with one or more concrete
examples or special cases. Commonly used methodologies based on the
theory are presented in methodological interludes scattered
throughout the book, along with a wealth of exercises that will
benefit students and instructors alike. Generalized inverses are
used throughout, so that the model matrix and various other
matrices are not required to have full rank. Considerably more
emphasis is given to estimability, partitioned analyses of
variance, constrained least squares, effects of model
misspecification, and most especially prediction than in many other
textbooks on linear models. This book is intended for master and
PhD students with a basic grasp of statistical theory, matrix
algebra and applied regression analysis, and for instructors of
linear models courses. Solutions to the book's exercises are
available in the companion volume Linear Model Theory - Exercises
and Solutions by the same author.
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