© 2010 – Chapman and Hall/CRC
240 pages | 50 B/W Illus.
Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related models.
Thoroughly revised and updated, An Introduction to Generalized Linear Models, Second Edition continues to initiate intermediate students of statistics, and the many other disciplines that use statistics, in the practical use of these models and methods. The new edition incorporates many of the important developments of the last decade, including survival analysis, nominal and ordinal logistic regression, generalized estimating equations, and multi-level models. It also includes modern methods for checking model adequacy and examples from an even wider range of application.
Statistics can appear to the uninitiated as a collection of unrelated tools. An Introduction to Generalized Linear Models, Second Edition illustrates how these apparently disparate methods are examples or special cases of a conceptually simple structure based on the exponential family of distribution, maximum likelihood estimation, and the principles of statistical modelling.
" The second edition … is successful in, filling a void in the otherwise sparse literature on the subject of generalized linear models at the introductory level … a wide range of research applications are covered and ample workings are also provided to aid the reader in statistical calculations … I would highly recommend this text for a reader interested in finding out at an introductory level what the subject area of generalized linear models is all about, including the non-statistician, undergraduate and graduate-level student."
-Kerrie Nelson, Department of Statistics, LeConte College, University of South Carolina, Columbia, USA, in Statistics in Medicine, Vol. 23, 2004
"… a unique and useful text for intermediate undergraduate teaching."
-Times Higher Education Supplement
"…I liked Dobson's basic and relatively brief presentation…Thanks go to the publisher for the softcover edition and attendant modest price, another of the book's virtues besides its brevity. These attributes make this book a recommended purchase for those who need a book on logistic regression. It is a good place to start."
-Technometrics, November 2002
Distributions Related to the Normal Distribution
Some Principles of Statistical Modelling
Notation and Coding for Explanatory Variables
EXPONENTIAL FAMILY AND GENERALIZED LINEAR
Exponential Family of Distributions
Properties of Distributions in the Exponential Family
Generalized Linear Models
Example: Failure Times for Pressure Vessels
Maximum Likelihood Estimation
Poisson Regression Example
Sampling Distribution for Score Statistics
Taylor Series Approximations
Sampling Distribution for Maximum Likelihood Estimators
Log-Likelihood Ratio Statistic
Sampling Distribution for the Deviance
NORMAL LINEAR MODELS
Multiple Linear Regression
Analysis of Variance
Analysis of Covariance
General Linear Models
BINARY VARIABLES AND LOGISTIC REGRESSION
Generalized Linear Models
Dose Response Models
General Logistic Regression Model
Goodness of Fit Statistics
Example: Senility and WAIS
NOMINAL AND ORDINAL LOGISTIC REGRESSION
Nominal Logistic Regression
Ordinal Logistic Regression
COUNT DATA, POISSON REGRESSION, AND LOG-LINEAR MODELS
Examples of Contingency Tables
Probability Models for Contingency Tables
Inference for Log-Linear Models
Survivor Functions and Hazard Functions
Empirical Survivor Function
Example: Remission Times
clustered and longitudinal data
Example: Recovery from Stroke
Repeated Measures Models for Normal Data
Repeated Measures Models for NON-NORMAL DATA
Stroke Example Continued