The Wiley–Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

". . .Variance Components is an excellent book. It is organized and well written, and provides many references to a variety of topics. I recommend it to anyone with interest in linear models."
Journal of the American Statistical Association

"This book provides a broad coverage of methods for estimating variance components which appeal to students and research workers . . . The authors make an outstanding contribution to teaching and research in the field of variance component estimation."
Mathematical Reviews

"The authors have done an excellent job in collecting materials on a broad range of topics. Readers will indeed gain from using this book . . . I must say that the authors have done a commendable job in their scholarly presentation."
Technometrics

This book focuses on summarizing the variability of statistical data known as the analysis of variance table. Penned in a readable style, it provides an up–to–date treatment of research in the area. The book begins with the history of analysis of variance and continues with discussions of balanced data, analysis of variance for unbalanced data, predictions of random variables, hierarchical models and Bayesian estimation, binary and discrete data, and the dispersion mean model.

Synopsis

This text presents a broad coverage of variance components. It deals with the estimation of variance components and the prediction of realized but unobservable values of random variables in analysis of variance models and in binary and discrete data. The authors begin with an introduction to the subject, which details more complicated types of data appearing in subsequent chapters. All the major methods of estimating components are discussed at length, including ANOVA, ML, REML, and Bayes. Topics covered include history, analysis of variance estimation, maximum likelihood (ML) estimation, prediction in mixed models, Bayes estimation and hierarchical models, categorical data, covariance components and minimum norm estimation, dispersion-mean model, kurtosis and fourth moments.

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