Linear Model Theory
Language: English
Published by John Wiley & Sons Inc, 2006
- Hardcover
- New
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Add to basketItem description from seller
Fundamentals of Multivariate Linear Models: Theory and Application consists of five parts. Part 1 centers on brief, clear mathematical statements of notation, assumptions, and formulas. Real data examples illustrate and motivate students. Num Pages: 410 pages, illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 241 x 161 x 24. Weight in Grams: 694. . 2006. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
Seller Inventory # V9780471214885
- Title
- Linear Model Theory
- Author
- Keith E. Muller
- Publisher
- John Wiley & Sons Inc
- Publication year
- 2006
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0471214884
- ISBN 13
- 9780471214885
Statisticians often use linear models for data analysis and for developing new statistical methods. Most books on the subject have historically discussed univariate, multivariate, and mixed linear models separately, whereas Linear Model Theory: Univariate, Multivariate, and Mixed Models presents a unified treatment in order to make clear the distinctions among the three classes of models.
Linear Model Theory: Univariate, Multivariate, and Mixed Models begins with six chapters devoted to providing brief and clear mathematical statements of models, procedures, and notation. Data examples motivate and illustrate the models. Chapters 7-10 address distribution theory of multivariate Gaussian variables and quadratic forms. Chapters 11-19 detail methods for estimation, hypothesis testing, and confidence intervals. The final chapters, 20-23, concentrate on choosing a sample size. Substantial sets of excercises of varying difficulty serve instructors for their classes, as well as help students to test their own knowledge.
The reader needs a basic knowledge of statistics, probability, and inference, as well as a solid background in matrix theory and applied univariate linear models from a matrix perspective. Topics covered include:
- A review of matrix algebra for linear models
- The general linear univariate model
- The general linear multivariate model
- Generalizations of the multivariate linear model
- The linear mixed model
- Multivariate distribution theory
- Estimation in linear models
- Tests in Gaussian linear models
- Choosing a sample size in Gaussian linear models
Filling the need for a text that provides the necessary theoretical foundations for applying a wide range of methods in real situations, Linear Model Theory: Univariate, Multivariate, and Mixed Models centers on linear models of interval scale responses with finite second moments. Models with complex predictors, complex responses, or both, motivate the presentation.
"Synopsis" may belong to another edition of this title.
About the Author
PAUL W. STEWART, PhD, is Research Associate Professor of Biostatistics at The University of North Carolina at Chapel Hill.
"About the title" may belong to another edition of this title.
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