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Variance Components

Shayle R. Searle, George Casella, Charles E. McCulloch

ISBN: 978-0-470-00959-8 April 2006 501 Pages




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."

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.

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This item: Variance Components

Linear Models

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Linear Models for Unbalanced Data

(Paperback £115.00)

Matrix Algebra Useful for Statistics

(Paperback £115.00)

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History and Comment.

The 1-Way Classification.

Balanced Data.

Analysis of Variance Estimation for Unbalanced Data.

Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML).

Prediction of Random Variables.

Computing ML and REML Estimates.

Hierarchical Models and Bayesian Estimation.

Binary and Discrete Data.

Other Procedures.

The Dispersion-Mean Model.



List of Tables and Figures.