Generalized, Linear, and Mixed Models / Edition 2

Hardcover (Print)
Used and New from Other Sellers
Used and New from Other Sellers
from $116.12
Usually ships in 1-2 business days
(Save 37%)
Other sellers (Hardcover)
  • All (10) from $116.12   
  • New (8) from $128.97   
  • Used (2) from $116.12   


This book provides a unified treatment of the use of mixed models for analyzing correlated data. Models for non-normal data, i.e. binary or count data, and generalized linear and nonlinear models are described and illustrated. The first few chapters of the book introduce all the major ideas in a context that will be familiar to most students of statistics. This text provides an accessible treatment of many of the newer statistical models for correlated, non-normally distributed data. The book's unified treatment addresses the needs of applications-oriented users of statistical packages and also graduate students in statistics.

Read More Show Less

Editorial Reviews

From the Publisher
"I strongly recommend…[it] for inclusion in math and statistics libraries and in the personal libraries of professional statisticians." (Journal of the American Statistical Association, December 2006)

"…well written and suitable to be a textbook…I enjoyed reading this book and recommend it highly to statisticians." (Journal of Statistical Computation and Simulation, January 2006)

"This text is to be highly recommended as one that provides a modern perspective on fitting models to data." (Short Book Reviews, Vol. 21, No. 2, August 2001)

"For graduate students and statisticians, McCulloch and Searle begin by reviewing the basics of linear models and linear mixed models..." (SciTech Book News, Vol. 25, No. 4, December 2001)

"...a very good reference book." (Zentralblatt MATH, Vol. 964, 2001/14)

"...another fine contribution to the statistics literature from these respected authors..." (Technometrics, Vol. 45, No. 1, February 2003)

Read More Show Less

Product Details

Meet the Author

Charles E. McCulloch, PhD, is Professor and Head of the Division of Biostatistics in the School of Medicine at the University of California, San Francisco. A Fellow of the American Statistical Association, Dr. McCulloch is the author of numerous published articles in the areas of longitudinal data analysis, generalized linear mixed models, and latent class models and their applications.

Shayle R. Searle, PhD, is Professor Emeritus in the Department of Biological Statistics and Computational Biology at Cornell University. Dr. Searle is the author of Linear Models, Linear Models for Unbalanced Data, Matrix Algebra Useful for Statistics, and Variance Components, all published by Wiley.

John M. Neuhaus, PhD, is Professor of Biostatistics in the School of Medicine at the University of California, San Francisco. A Fellow of the American Statistical Association and the Royal Statistical Society, Dr. Neuhaus has authored or coauthored numerous journal articles on statistical methods for analyzing correlated response data and assessments on the effects of statistical model misspecification.

Read More Show Less

Table of Contents


Preface to the First Edition.

1. Introduction.

1.1 Models.

1.2 Factors, Levels, Cells, Effects And Data.

1.3 Fixed Effects Models.

1.4 Random Effects Models.

1.5 Linear Mixed Models (Lmms).

1.6 Fixed Or Random?

1.7 Inference.

1.8 Computer Software.

1.9 Exercises.

2. One-Way Classifications.

2.1 Normality And Fixed Effects.

2.2 Normality, Random Effects And MLE.

2.3 Normality, Random Effects And REM1.

2.4 More On Random Effects And Normality.

2.5 Binary Data: Fixed Effects.

2.6 Binary Data: Random Effects.

2.7 Computing.

2.8 Exercises.

3. Single-Predictor Regression.

3.1 Introduction.

3.2 Normality: Simple Linear Regression.

3.3 Normality: A Nonlinear Model.

3.4 Transforming Versus Linking.

3.5 Random Intercepts: Balanced Data.

3.6 Random Intercepts: Unbalanced Data.

3.7 Bernoulli - Logistic Regression.

3.8 Bernoulli - Logistic With Random Intercepts.

3.9 Exercises.

4. Linear Models (LMs).

4.1 A General Model.

4.2 A Linear Model For Fixed Effects.

4.3 Mle Under Normality.

4.4 Sufficient Statistics.

4.5 Many Apparent Estimators.

4.6 Estimable Functions.

4.7 A Numerical Example.

4.8 Estimating Residual Variance.

4.9 Comments On The 1- And 2-Way Classifications.

4.10 Testing Linear Hypotheses.

4.11 T-Tests And Confidence Intervals.

4.12 Unique Estimation Using Restrictions.

4.13 Exercises.

5. Generalized Linear Models (GLMs).

5.1 Introduction.

5.2 Structure Of The Model.

5.3 Transforming Versus Linking.

5.4 Estimation By Maximum Likelihood.

5.5 Tests Of Hypotheses.

5.6 Maximum Quasi-Likelihood.

5.7 Exercises.

6. Linear Mixed Models (LMMs).

6.1 A General Model.

6.2 Attributing Structure To VAR(y).

6.3 Estimating Fixed Effects For V Known.

6.4 Estimating Fixed Effects For V Unknown.

6.5 Predicting Random Effects For V Known.

6.6 Predicting Random Effects For V Unknown.

6.7 Anova Estimation Of Variance Components.

6.8 Maximum Likelihood (Ml) Estimation.

6.9 Restricted Maximum Likelihood (REMl).

6.10 Notes And Extensions.

6.11 Appendix For Chapter 6.

6.12 Exercises.

7. Generalized Linear Mixed Models.

7.1 Introduction.

7.2 Structure Of The Model.

7.3 Consequences Of Having Random Effects.

7.4 Estimation By Maximum Likelihood.

7.5 Other Methods Of Estimation.

7.6 Tests Of Hypotheses.

7.7 Illustration: Chestnut Leaf Blight.

7.8 Exercises.

8. Models for Longitudinal data.

8.1 Introduction.

8.2 A Model For Balanced Data.

8.3 A Mixed Model Approach.

8.4 Random Intercept And Slope Models.

8.5 Predicting Random Effects.

8.6 Estimating Parameters.

8.7 Unbalanced Data.

8.8 Models For Non-Normal Responses.

8.9 A Summary Of Results.

8.10 Appendix.

8.11 Exercises.

9. Marginal Models.

9.1 Introduction.

9.2 Examples Of Marginal Regression Models.

9.3 Generalized Estimating Equations.

9.4 Contrasting Marginal And Conditional Models.

9.5 Exercises.

10. Multivariate Models.

10.1 Introduction.

10.2 Multivariate Normal Outcomes.

10.3 Non-Normally Distributed Outcomes.

10.4 Correlated Random Effects.

10.5 Likelihood Based Analysis.

10.6 Example: Osteoarthritis Initiative.

10.7 Notes And Extensions.

10.8 Exercises.

11. Nonlinear Models.

11.1 Introduction.

11.2 Example: Corn Photosynthesis.

11.3 Pharmacokinetic Models.

11.4 Computations For Nonlinear Mixed Models.

11.5 Exercises.

12. Departures From Assumptions.

12.1 Introduction.

12.2 Misspecifications Of Conditional Model For Response.

12.3 Misspecifications Of Random Effects Distribution.

12.4 Methods To Diagnose And Correct For Misspecifications.

12.5 Exercises.

13. Prediction.

13.1 Introduction.

13.2 Best Prediction (BP).

13.3 Best Linear Prediction (BLP).

13.4 Linear Mixed Model Prediction (BLUP).

13.5 Required Assumptions.

13.6 Estimated Best Prediction.

13.7 Henderson’s Mixed Model Equations.

13.8 Appendix.

13.9 Exercises.

14. Computing.

14.1 Introduction.

14.2 Computing Ml Estimates For LMMs.

14.3 Computing Ml Estimates For GLMMs.

14.4 Penalized Quasi-Likelihood And Laplace.

14.5 Exercises.

Appendix M: Some Matrix Results.

M.1 Vectors And Matrices Of Ones.

M.2 Kronecker (Or Direct) Products.

M.3 A Matrix Notation.

M.4 Generalized Inverses.

M.5 Differential Calculus.

Appendix S: Some Statistical Results.

S.1 Moments.

S.2 Normal Distributions.

S.3 Exponential Families.

S.4 Maximum Likelihood.

S.5 Likelihood Ratio Tests.

S.6 MLE Under Normality.



Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star


4 Star


3 Star


2 Star


1 Star


Your Rating:

Your Name: Create a Pen Name or

Barnes & Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation


  • - By submitting a review, you grant to Barnes & and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Terms of Use.
  • - Barnes & reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)