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Variance Components, by Shayle Robert Searle, George Casella, Charles E. McCulloch
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WILEY-INTERSCIENCE PAPERBACK SERIES
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.
- Sales Rank: #1869706 in Books
- Published on: 1992-03-13
- Ingredients: Example Ingredients
- Original language: English
- Number of items: 1
- Dimensions: 9.74" h x 1.11" w x 6.42" l, .0 pounds
- Binding: Hardcover
- 528 pages
From the Publisher
This volume is written on the subject of the summarizing of 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. Starts 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.
From the Back Cover
WILEY-INTERSCIENCE PAPERBACK SERIES
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.
About the Author
SHAYLE R. SEARLE, PhD, is Professor Emeritus of Biometry at Cornell University. He is the author of Linear Models, Linear Models for Unbalanced Data, and Matrix Algebra Useful for Statistics, all from Wiley.
GEORGE CASELLA, PhD, is Professor and Chair of the Department of Statistics at the University of Florida. His research interests include decision theory and statistical confidence.
CHARLES E. McCULLOCH, PhD, is Professor of Biostatistics at the University of California, San Francisco. He is the author of numerous scientific publications on biometrics and bio-logical statistics. He is a coauthor, with Shayle R. Searle, of Generalized, Linear, and Mixed Models (Wiley 2001).
Most helpful customer reviews
0 of 0 people found the following review helpful.
Also the book was in great physical shape
By Nicholas Santantonio
This book is very rigorous and well structured. A must for any statistician that cares about dispersion. Also the book was in great physical shape.
5 of 8 people found the following review helpful.
Must have book for process engineers.
By SamHartley@HartDevCo.com
This book covers the history and application of one of the most important, but underutilized statistical methods for engineers. Variance components will give you insight unmatched by the typical methods of analysis currently in use. The explanations are readable by engineers with one to two years of statistical methods experience. This is a must have for semiconductor(any?) manufacturing process or product engineer.
1 of 1 people found the following review helpful.
Variance compponents Book
By Alexander Pswarayi
This is an easy to read book on a complex subject. It is a must for those doing research related to variance components. In particular, I like the way the author developed the topic. In particular the exposition of variance components' development as a subject over time from the early days (in the 1800)to date. This helped me in understanding the subject.
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