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Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.
Along with describing common applications of GLMMs, the text introduces the essential theory and main methodology associated with linear models that accommodate random model effects and non-Gaussian data. Unlike traditional linear model textbooks that focus on normally distributed data, this one adopts a generalized mixed model approach throughout: data for linear modeling need not be normally distributed and effects may be fixed or random.
With numerous examples using SAS® PROC GLIMMIX, this book is ideal for graduate students in statistics, statistics professionals seeking to update their knowledge, and researchers new to the generalized linear model thought process. It focuses on data-driven processes and provides context for extending traditional linear model thinking to generalized linear mixed modeling.
See Professor Stroup discuss the book.
- Sales Rank: #659933 in Books
- Brand: Brand: CRC Press
- Published on: 2012-09-24
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 1.20" w x 7.10" l, 2.45 pounds
- Binding: Hardcover
- 555 pages
- Used Book in Good Condition
Review
"The book focuses on data-driven modeling and design processes, and it provides a context for extending traditional linear model thinking to generalised linear mixed modeling. This is a very sound text which teachers of any course on GLMMs should consider adopting."
―Erkki P. Liski, International Statistical Review (2013), 81
"Walter Stroup is a leading authority on GLMMs for applied statisticians, especially as implemented in the SAS programming environment. He offers a thorough, engaging, and opinionated treatment of the subject … I found the ‘fully general’ GLMM approach to modeling and design issues (Chapters 1 and 2) to be quite illuminating. … it is best to use this text in conjunction with SAS. Prospective readers without current access to SAS will be pleased to know that a reasonable level of access to SAS is now available at no cost to students and teachers on the web … If the reader prefers to work with GLMMs in the free, powerful, and state-of-the-art R environment, then he/she should supplement this text with some others that are built around R. I myself had good luck using Stroup’s text along with Julian Faraway’s two books Linear Models with R and Expanding the Linear Model with R, both published by CRC Press."
―Homer White, MAA Reviews, June 2013
"… for SAS users concerned with the analysis of trials, it is a very good resource. There are excellent discussions on many important concepts such as likelihood ratio testing and model selection criteria. PROC GLIMMIX is a powerful procedure implementing the rich family of GLMMs, and this book gives coverage to a wide variety of models with ample software illustration."
―Gillian Z. Heller, Australian & New Zealand Journal of Statistics, 2013
About the Author
Walter W. Stroup, Ph.D., is currently professor in the Department of Statistics at the University of Nebraska. His responsibilities include teaching statistical modeling, design of experiments, and research applications of mixed models in collaboration with researchers in agriculture, natural resources, medical and pharmaceutical sciences, education, and the behavioral sciences. Dr. Stroup received a B.A. degree in psychology from Antioch College, and M.S. and Ph.D. degrees in statistics from the University of Kentucky. He is widely published in statistical and applied journals and has participated in a number of symposia on issues in statistical consulting and statistical modeling. He has taught numerous short courses and workshops on generalized linear mixed models. A SAS user since 1975, Dr. Stroup is coauthor of SAS for Linear Models, Fourth Edition, SAS for Mixed Models, both editions. In additional, Dr. Stroup is author of Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, an introduction to GLMMs that makes extensive use of SAS examples.
Most helpful customer reviews
15 of 15 people found the following review helpful.
Outstanding, and not just for SAS users
By Dr Joseph A. Bulbulia
I am an R user, and resisted buying a book on GLMMs with examples in SAS. But I heard about this book on R's mixed model mailing list (no less than Doug Bates gave it a plug). I decided to give it a read - boy was I surprised! This book has helped me to understand GLMMs like no other.
Here's what's good:
1. The book starts with the view that linear regression is a special case of GLMM, and builds a simple, clear, and solid exposition from this assumption. The author's case is persuasive; I think students (and others) coming at *regression* for the first time will benefit from this approach.
2. The first part of the book (esp. chapters 2) develops GLMM in the context of experimental design, with application's to "nature's experiments" -- ecology, longitudinal designs, panel data, etc. The focus on developing a good and appropriate linear predictor with a clear appreciation of the peculiarities of each specific design has strongly improved my understanding of GLMMs (and more basically of *regression*). I especially appreciated Stroup's advice on sketching a plot plan for each design. There's also a plain and persuasive argument on the importance of distinguishing between units of observation and units of replication/randomization when constructing any regression model.
3. The text is written cleanly. Few words are wasted. Stroup's dry sense of humor makes the book a pleasure to read, and fun! No BS. You get the feeling you'd like this guy.
4. Even the SAS parts were helpful (hence the surprise!) This is because Stroup works through the issues of how the computations are performed in light of the specificities of a model. Again his point is that you need to understand how to construct a good and appropriate linear predictor. No receipts for that. Oddly then, I think the SAS parts are worth reading and understanding regardless of whatever platform you use.
Drawbacks:
1. As a newbie to GLMM, I found I had to re-read some parts, and I feel I could use another read through it. Those who are coming at GLMMs for the first time should expect to move slowly. Those who understand GLMMs (or think they do) but are not used to concentrating carefully about design questions, will also want to move slowly. No canned solutions.
4 of 4 people found the following review helpful.
A LOT of mistakes/ typos, but still helpful
By WSK
I purchased this book based on the positive reviews I read online, and I agree that this can be a very helpful stats and SAS textbook, but it lost points due to an abundance of errors and typos. Also, this is a very dense textbook, so it can be tricky if attempting to work through it on your own (rather than guided through it as part of a stats course).
It is still very helpful in describing the theory behind the model formation and selection. And I really like that the example SAS codes he uses in the book are available to download (for free) from the publishers website (http://www.crcpress.com/product/isbn/9781439815120).
The reason I'm only giving it 3/5 stars is because of how many mistakes and types are in the textbook (and in the SAS code available online, which as of now was last updated Oct 22, 2012), both which can seriously impair understanding and take a while to recognize/fix.
Some key examples are:
1) The data and output data set names give in the "Chapter_1_Table_1_1" SAS example file are incorrect. The code doesn't work as written; you have to fix the names for it to run correctly.
2) The SAS code provided for Example 3.5 (using data set 3.2) does not include the variables a or b. These need to be reconstructed based on the table at the bottom of page 85. However, following the outline of the table, the SAS output results for a and b are backwards. So to get the correct output as given in the book, the treatments need to be as follows:
treatment 0 = A0, B0; treatment 1 = A0, B1; treatment 2 = A1, B0; treatment 3 = A1, B1
These examples don't include the many typos in the actual text.
Also, since I wasn't as familiar with matrix algebra, I had to look online for a lot of help and clarification, especially early on.
But since this is forcing me to brush up on basic stats knowledge, as well as helping me understand more advanced concepts, I'm still giving it a conditional recommendation.
4 of 4 people found the following review helpful.
Explains the relationship between study design and analysis
By Chris Macintosh
I'm still working my way through the text but I give it a high recommendation. This is one of the first texts I've found to give a step by step description about matching study design with analysis plan. I appreciate the clear explanations and concrete examples. I lack an extensive preparation in statistics so there are some things I need to look up as I'm reading, but I appreciate the clear explanations of analyses with data that are not normally distributed. I also appreciate the periodic translation of terminology from other fields into what they're talking about (i.e. hierarchical linear modeling and social science). Thank you for making a challenging topic a bit easier to understand!
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