Variance components
"This book presents broad coverage of variance components estimation and mixed models. Its chapters cover history (Chapter 2), analysis of variance estimation (Chapters 3, 4, and 5), maximum likelihood (ML) estimation, including restricted ML and computational methods (Chapters 6 and 8), prediction in mixed models (Chapter 7), Bayes estimation and hierarchical models (Chapter 9), categorical data (Chapter 10), covariance components and minimum norm estimation (Chapter 11), and finally, the dispersion-mean model, kurtosis and fourth moments (Chapter 12). Estimation from balanced data (having the same number of observations in the subclasses) is dealt with fully in Chapter 4, and in parts of Chapters 3 and 12; and elsewhere, estimation from unbalanced data (having unequal numbers of observations in the subclasses) is dealt with at great length with numerous details for the 1-way and 2-way classifications."
Print Book, English, ©1992
Wiley, New York, ©1992