Searle / Casella / McCulloch Variance Components

1. Auflage 2009
ISBN: 978-0-470-31769-3
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 536 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

WILEY-INTERSCIENCE PAPERBACK SERIES
The Wiley-Interscience Paperback Series consists ofselected books that have been made more accessible to consumers inan effort to increase global appeal and general circulation. Withthese new unabridged softcover volumes, Wiley hopes to extend thelives of these works by making them available to future generationsof statisticians, mathematicians, and scientists.
". . .Variance Components is an excellent book. It isorganized and well written, and provides many references to avariety of topics. I recommend it to anyone with interest in linearmodels."
--Journal of the American StatisticalAssociation
"This book provides a broad coverage of methods for estimatingvariance components which appeal to students and research workers .. . The authors make an outstanding contribution to teaching andresearch in the field of variance component estimation."
--Mathematical Reviews
"The authors have done an excellent job in collecting materialson a broad range of topics. Readers will indeed gain from usingthis book . . . I must say that the authors have done a commendablejob in their scholarly presentation."
--Technometrics
This book focuses on summarizing the variability of statisticaldata known as the analysis of variance table. Penned in a readablestyle, it provides an up-to-date treatment of research in the area.The book begins with the history of analysis of variance andcontinues with discussions of balanced data, analysis of variancefor unbalanced data, predictions of random variables, hierarchicalmodels and Bayesian estimation, binary and discrete data, and thedispersion mean model.

Autoren/Hrsg.

Searle, Shayle R.

Casella, George

McCulloch, Charles E.

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 Autor(innen)

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).