Linear Models and the Relevant Distributions and Matrix Algebra
provides in-depth and detailed coverage of the use of linear
statistical models as a basis for parametric and predictive
inference. It can be a valuable reference, a primary or secondary
text in a graduate-level course on linear models, or a resource
used (in a course on mathematical statistics) to illustrate various
theoretical concepts in the context of a relatively complex setting
of great practical importance. Features: Provides coverage of
matrix algebra that is extensive and relatively self-contained and
does so in a meaningful context Provides thorough coverage of the
relevant statistical distributions, including spherically and
elliptically symmetric distributions Includes extensive coverage of
multiple-comparison procedures (and of simultaneous confidence
intervals), including procedures for controlling the k-FWER and the
FDR Provides thorough coverage (complete with detailed and highly
accessible proofs) of results on the properties of various
linear-model procedures, including those of least squares
estimators and those of the F test. Features the use of real data
sets for illustrative purposes Includes many exercises David
Harville served for 10 years as a mathematical statistician in the
Applied Mathematics Research Laboratory of the Aerospace Research
Laboratories at Wright-Patterson AFB, Ohio, 20 years as a full
professor in Iowa State University's Department of Statistics where
he now has emeritus status, and seven years as a research staff
member of the Mathematical Sciences Department of IBM's T.J. Watson
Research Center. He has considerable relevant experience, having
taught M.S. and Ph.D. level courses in linear models, been the
thesis advisor of 10 Ph.D. graduates, and authored or co-authored
two books and more than 80 research articles. His work has been
recognized through his election as a Fellow of the American
Statistical Association and of the Institute of Mathematical
Statistics and as a member of the International Statistical
Institute.
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