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An Introduction to Generalized Linear Models
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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 to GLM (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 GLM

 This 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.


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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
Key features

- Provides an accessible but thorough introduction to the generalized linear models, exponential family distribution, and maximum likelihood estimation

- Includes discussion on checking model adequacy and description on how to use a popular statistical software program, SAS, to fit generalized linear models

- Describes the connection between survival analysis and generalized linear models  

 


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