Generalized, Linear, and Mixed Models
1st Edition
Published on 1. January 2001
Book
Hardback
358 pages
978-0-471-19364-7 (ISBN)
Description
Generalized linear models are still the primary tools of statistical analysis and form the underpinning of generalized linear models. This book provides a self-contained reference on the dichotomy of linear and generalized linear models with fixed and mixed effects.
Reviews / Votes
"This text is to be highly recommended as one that provides a modern perspective on fitting models to data." (Short Book Reviews, Vol. 21, No. 2, August 2001) "For graduate students and...statisticians, McCulloch and Searle begin by reviewing the basics of linear models and linear mixed models..." (SciTech Book News, Vol. 25, No. 4, December 2001) "...a very good reference book." (Zentralblatt MATH, Vol. 964, 2001/14) "...another fine contribution to the statistics literature from these respected authors..." (Technometrics, Vol. 45, No. 1, February 2003)More details
Series
Edition
1., Aufl.
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
Ill.
Dimensions
Height: 24.5 cm
Width: 16.4 cm
Thickness: 23 mm
Weight
624 gr
ISBN-13
978-0-471-19364-7 (9780471193647)
Schweitzer Classification
Other editions
New editions
Charles E. McCulloch | Shayle R. Searle | John M. Neuhaus
Generalized, Linear, and Mixed Models
Book
07/2008
2nd Edition
Wiley
€199.00
Shipment within 10-20 days
Persons
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 biological statistics and a coauthor (with Shayle Searle and George Casella) of Variance Components (Wiley).
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.
Content
Preface. Introduction. One--Way Classifications. Single--Predictor Regression. Linear Models (LMs). Generalized Linear Models (GLMs). Linear Mixed Models (LMMs). Longitudinal Data. GLMMs. Prediction. Computing. Nonlinear Models. Appendix M: Some Matrix Results. Appendix S: Some Statistical Results. References. Index.