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Books > Business & Economics > Economics > Econometrics > Economic statistics
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A First Course in Linear Model Theory (Hardcover, 2nd edition)
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A First Course in Linear Model Theory (Hardcover, 2nd edition)
Series: Chapman & Hall/CRC Texts in Statistical Science
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Thoroughly updated throughout, A First Course in Linear Model
Theory, Second Edition is an intermediate-level statistics text
that fills an important gap by presenting the theory of linear
statistical models at a level appropriate for senior undergraduate
or first-year graduate students. With an innovative approach, the
authors introduce to students the mathematical and statistical
concepts and tools that form a foundation for studying the theory
and applications of both univariate and multivariate linear models.
In addition to adding R functionality, this second edition features
three new chapters and several sections on new topics that are
extremely relevant to the current research in statistical
methodology. Revised or expanded topics include linear fixed,
random and mixed effects models, generalized linear models,
Bayesian and hierarchical linear models, model selection, multiple
comparisons, and regularized and robust regression. New to the
Second Edition: Coverage of inference for linear models has been
expanded into two chapters. Expanded coverage of multiple
comparisons, random and mixed effects models, model selection, and
missing data. A new chapter on generalized linear models (Chapter
12). A new section on multivariate linear models in Chapter 13, and
expanded coverage of the Bayesian linear models and longitudinal
models. A new section on regularized regression in Chapter 14.
Detailed data illustrations using R. The authors' fresh approach,
methodical presentation, wealth of examples, use of R, and
introduction to topics beyond the classical theory set this book
apart from other texts on linear models. It forms a refreshing and
invaluable first step in students' study of advanced linear models,
generalized linear models, nonlinear models, and dynamic models.
General
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