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.
Inhaltsverzeichnis
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.
Appendices.
References.
List of Tables and Figures.
Indexes.
Über den Autor
SHAYLE R. SEARLE, Ph D, 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, Ph D, 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. Mc CULLOCH, Ph D, 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).
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