Linear Model Methodology

Andre I. Khuri is a Professor Emeritus in the Department of Statistics at the University of Florida in Gainesville.

This is a comprehensive and up-to-date textbook on the theory of linear models. ... Every chapter besides the first historical one contains many exercises ... . There is also a huge bibliography. The textbook represents an important source for all researchers and lectures in linear models. -Hilmar Drygas, Zentralblatt MATH, 2012 The outstanding book, written by a prominent researcher and author, presents a wealth of materials on linear models in Chapters 1 though 12 and includes materials on generalized linear models in the last chapter. The material on linear models is an amazing collection of important topics that would benefit researchers, teachers, students, and practitioners and has added value to the book. Many illustrative examples are presented with SAS codes. The examples are practically important and thoughtfully chosen. Exercises at the end of Chapters 2-13 are excellent, and some are valuable resources for the researchers in this area. ... The author has to be commended for his success in executing this so elegantly. -Subir Ghosh, Journal of Quality Technology, Vol. 44, January 2012 This book provides a very well-written and rigorous account of the theory of linear models. ... In sum, this is a carefully written and reliable book that reflects the experience of the author in teaching graduate level courses on linear models. I will certainly add it to the list of reference textbooks for the graduate one-quarter course on linear model theory taught at UC Santa Cruz. -Raquel Prado, Journal of the American Statistical Association, September 2011, Vol. 106 This text is a possible choice for a second course in linear model theory. -David J. Olive, Technometrics, May 2011 The material is well chosen and well organized, and includes many results that are not found in other textbooks. ... Throughout the book, the presentation is very clear and well organized, with a focus on mathematical developments. Most results are stated with proofs, some material is based on the author's own contributions to the field. Generally, many important special cases are treated in detail, which will make the book also highly useful as a reference. There are also many worked-out examples from different subject areas to illustrate the methods. Later chapters also include some instructions on how to use the methods in SAS. Furthermore, there are lots of exercises at the end of each chapter. ... The book is very accessible and encompassing ... the book will be an excellent choice both as a text and as a reference book. -T. Mildenberger, Statistical Papers, April 2011 The material on which this book is based has been taught in a couple of courses at the University of Florida for about 20 years and the author's skills and experience in doing this are superbly represented in this fine text. ... there are numerous exercises that reinforce both the theoretical and the practical aspects of regression... This is an excellent, reliable, and comprehensive text. -International Statistical Review (2010), 78 This book provides a thorough overview which is similar to other available texts but in a very different way. The choice of topics covered, their organization and presentation are the unique features that distinguish this book. ... This book is well structured as a textbook as well as a reference with every chapter explaining the definitions, principles and methods of the subject matter illustrated by data-based examples with the details on use of SAS software, wherever possible. ... the topics that are covered in Chapters 7-12 are not generally found in a single book. ... The book would make an excellent textbook for a course on linear models at masters and graduate levels. Moreover, some parts of the book can also be a part of a course on analysis of variance. Overall, the book is a valuable reference for those involved in research and teaching in this area. -Journal of the Royal Statistical Society, Series A, 2010

• Linear Models: Some Historical Perspectives. Basic Elements of Linear Algebra. Basic Concepts in Matrix Algebra. The Multivariate Normal Distribution. Quadratic Forms in Normal Variables. Full-Rank Linear Models. Less-Than-Full-Rank Linear Models. Balanced Linear Models. The Adequacy of Satterthwaite's Approximation. Unbalanced Fixed-Effects Models. Unbalanced Random and Mixed Models. Additional Topics in Linear Models. Generalized Linear Models. Bibliography. Index.