SAS for Mixed Models, Second Edition

Features

• Cover Type: Paperback with 840 pages
• Published by: SAS Publishing; 2 Pap/Cdr edition February 21, 2006
• Written in: English
• ISBN 10 Number: 1590475003
• ISBN 13 Number: 978-1590475003
• Book Dimensions: 10.9 x 8.3 x 1.3 inches
• Weighs: 3.4 pounds

Product Review
This is a revision of an already great text. The authors take time to motivate and explain the calculations being done. The examples are information rich, and I can see them serving as templates for a wide variety of applications. Each is followed by an interpretation section that is most helpful. Nonlinear and generalized linear mixed models are addressed, as are Bayesian methods, and some helpful suggestions are presented for dealing with convergence problems. Those familiar with the previous release will be excited to learn about the new features in PROC MIXED. The MIXED procedure has had a great influence on how statistical analyses are performed. It has allowed us to do correct analyses where we have previously been hampered by computational limitations. It is hard to imagine anyone claiming to be a modern professional data analyst without knowledge of the methods presented in this book. The mixed model pulls into a common framework many analyses of experimental designs and observational studies that have traditionally been treated as being different from each other. By describing the three model components X, Z, and the error term e, one can reproduce and often improve on the analysis of any designed experiment. I am looking forward to getting my published copy of the book and am sure it will be well worn in no time. --David A. Dickey, Professor of Statistics, North Carolina State University

Product Review
SAS for Mixed Models, Second Edition addresses the large class of statistical models with random and fixed effects. Mixed models occur across most areas of inquiry, including all designed experiments, for example. This book should be required reading for all statisticians, and will be extremely useful to scientists involved with data analysis. Most pages contain example output, with the capabilities of mixed models and SAS software clearly explained throughout. I have used the first edition of SAS for Mixed Models as a textbook for a second-year graduate-level course in linear models, and it has been well received by students. The second edition provides dramatic enhancement of all topics, including coverage of the new GLIMMIX and NLMIXED procedures, and a chapter devoted to power calculations for mixed models. The chapter of case studies will be interesting reading, as we watch the experts extract information from complex experimental data (including a microarray example). I look forward to using this superb compilation as a textbook. --Arnold Saxton, Department of Animal Science, University of Tennessee

Reader Reviews
Positive: 1.Well written in a field with limited sources. Negative: 2. Not enough explanations for a lot of procedures. 2. Expensive Comment | | (Report this)