First Course in Linear Model Theory

Systematically presents the basic theory of linear statistical models through the concepts and tools of matrix and linear algebra and distribution theoryIncludes tools relevant to topics beyond classical linear models, such as nonlinear regression, wavelets, and neural networks, that make the book a valuable reference for research statisticiansEmphasizes aspects of Bayesian methods for linear models and a rich family of elliptically contoured distributionsPresents stimulating examples of direct statistical relevanceContains numerous computational examples that provide readers with practice in data analysisSolutions Manual available with qualifying course adoption This innovative, intermediate-level statistics text 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 author's introduces students to the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear modelsA First Course in Linear Model Theory systematically presents the basic theory behind linear statistical models with motivation from an algebraic as well as a geometric perspective. Through the concepts and tools of matrix and linear algebra and distribution theory, it provides a framework for understanding classical and contemporary linear model theory. It does not merely introduce formulas, but develops in students the art of statistical thinking and inspires learning at an intuitive level by emphasizing conceptual understanding.The authors' fresh approach, methodical presentation, wealth of examples, 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.