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An Introduction to Generalized Linear Models
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An Introduction to Generalized Linear Models

  • George H. Dunteman
  • Moon-Ho R. Ho - Department of Psychology, McGill University, Montreal, Quebec, Canada Division of Psychology, Nanyang Technological University, Singapore


November 2005 | 88 pages | SAGE Publications, Inc
Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts toáGLM (including Poisson regression. logistic regression, and proportional hazards models) and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets, and the computer instructions and results will be presented for each example. Throughout the book, there is an emphasis on link functions and error distribution and how the model specifications translate into likelihood functions that can, through maximum likelihood estimation be used to estimate the regression parameters and their associated standard errors. This book provides readers with basic modeling principles that are applicable to a wide variety of situations.Key Features:- Provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation- Includes discussion on checking model adequacy and description on how to use SAS to fit GLM- Describes the connection between survival analysis and GLMáThis book is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.
 
List of Figures and Tables
 
Series Editor’s Introduction
 
Acknowledgments
 
1. Generalized Linear Models
 
2. Some Basic Modeling Concepts
Categorical Independent Variables

 
Essential Components of Regression Modeling

 
 
3. Classical Multiple Regression Model
Assumptions and Modeling Approach

 
Results of Regression Analysis

 
Multiple Correlation

 
Testing Hypotheses

 
 
4. Fundamentals of Generalized Linear Modeling
Exponential Family of Distributions

 
Classical Normal Regression

 
Logistic Regression

 
Poisson Regression

 
Proportional Hazards Survival Model

 
 
5. Maximum Likelihood Estimation
 
6. Deviance and Goodness of Fit
Using Deviances to Test Statistical Hypotheses

 
Goodness of Fit

 
Assessing Goodness of Fit by Residual Analysis

 
 
7. Logistic Regression
Example of Logistic Regression

 
 
8. Poisson Regression
Example of Poisson Regression Model

 
 
9. Survival Analysis
Survival Time Distributions

 
Exponential Survival Model

 
Example of Exponential Survival Model

 
 
Conclusions
 
Appendix
 
References
 
Index
 
About the Authors

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ISBN: 9780761920847
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