Linear Model Theory: Univariate, Multivariate, and Mixed Models (Wiley Series In Probability And Statistics)

Keith E. Muller, Paul W. Stewart

ISBN 10: 0471214884 / ISBN 13: 9780471214885

Editore: Wiley-Interscience 22 A, 2006

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Linear Model Theory: Univariate, Multivariate, and Mixed Models (Wiley Series In Probability And Statistics) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

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Dati bibliografici

• Titolo: Linear Model Theory: Univariate, Multivariate, and Mixed Models (Wiley Series In Probability And Statistics)
• Editore: Wiley-Interscience 22 A
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 0471214884
• ISBN 13: 9780471214885
• Rilegatura: Hardcover
• Condizione: Very Good

Descrizione articolo

Riassunto

A precise and accessible presentation of linear model theory, illustrated with data examples

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.

Informazioni sull?autore

KEITH E. MULLER, PhD, is Professor and Director of the Division of Biostatistics in the Department of Epidemiology and Health Policy Research in the College of Medicine at the University of Florida in Gainesville, as well as Professor Emeritus of Biostatistics at The University of North Carolina at Chapel Hill where the book was written.

PAUL W. STEWART, PhD, is Research Associate Professor of Biostatistics at The University of North Carolina at Chapel Hill.