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Abstract:
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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 e
"This 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." -- Back cover.
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