1. The need for more than one random-effect term when fitting a regression line.
2. The need for more than one random-effect term in a designed experiment.
3. Estimation of the variances of random-effect terms.
4. Interval estimates for fixed-effect terms in mixed models.
5. Estimation of random effects in mixed models: best linear unbiased predictors.
6. More advanced mixed models for more elaborate data sets.
7. Two case studies.
8. The use of mixed models for the analysis of unbalanced experimental designs.
9. Beyond mixed modelling.
10. Why is the criterion for fitting mixed models called residual maximum likelihood?
“The book provides a comprehensive introduction to mixed modelling, ideal for final year undergraduate students, postgraduate students and professional researchers alike. Readers will come from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, and social sciences.” (Zentralblatt MATH, 2012)
"This book would be useful for anyone who sues GenStat and/or R desiring an introduction to applied mixed modeling, and they should certainly have a look." (Technometrics, August 2008)
- Provides a straightforward introduction to mixed modelling techniques.
- Suitable for a wide audience of students and practitioners, from statistics, bioinformatics, medicine, industry and economics.
- Illustrates how the capabilities of regression analysis can be combined with those of ANOVA by the specification of a mixed model.
- Introduces to the criterion of Restricted Maximum Likelihood (REML) for the fitting of a mixed model to data.
- Introduced the application of mixed model analysis to a wide range of situations and how to obtain and interpret Best Linear Unbiased Predictors (BLUPs).
- Written by a well-respected consultant with extensive teaching experience.
- Includes exercises and solutions, enabling use for self-study or as a course text.
- Supported by a Website featuring full solutions to exercises, further examples, statistical software and data sets.