Rencher, Alvin C.
Linear Models in Statistics
Since most advanced statistical tools are generalizations of the linear model, it is necessary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed.
Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, twoway models with empty cells, geometry of least squares, vectormatrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models.
This modern Second Edition features:

New chapters on Bayesian linear models as well as random and mixed linear models

Expanded discussion of twoway models with empty cells

Additional sections on the geometry of least squares

Updated coverage of simultaneous inference
The book is complemented with easytoread proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples.
Linear Model in Statistics, Second Edition is a musthave book for courses in statistics, biostatistics, and mathematics at the upperundergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
 Author(s)
 Rencher, Alvin C.
 Schaalje, G. Bruce
 Publisher
 John Wiley and Sons, Inc.
 Publication year
 2008
 Language
 en
 Edition
 2
 Page amount
 544 pages
 Category
 Natural Sciences
 Format
 Ebook
 eISBN (PDF)
 9780470192603
 Printed ISBN
 9780471754985