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Generalized Linear Models: A Unified Approach provides an introduction to and overview of GLMs, with each chapter carefully laying the groundwork for the next. The Second Edition provides examples using real data from multiple fields in the social sciences such as psychology, education, economics, and political science, including data on voting intentions in the 2016 U.S. Republican presidential primaries. The Second Edition also strengthens material on the exponential family form, including a new discussion on multinomial distribution; adds more information on how to interpret results and make inferences in the chapter on estimation procedures; and has a new section on extensions to generalized linear models.

Software scripts, supporting documentation, data for the examples, and some extended mathematical derivations are available on the authors’ websites (http://jeffgill.org/publications/generalized-linear-models-unified-approach-0) as well as through the \texttt{R} package \texttt{GLMpack}.

 
Series Editor's Introduction
 
About the Authors
 
Acknowledgements
 
1. Introduction
Model Specification  
Prerequisites and Preliminaries  
Looking Forward  
 
2. The Exponential Family
Justification  
Derivation of the Exponential Family Form  
Canonical Form  
Multi-Parameter Models  
 
3. Likelihood Theory and the Moments
Maximum Likelihood Estimation  
Calculating the Mean of the Exponential Family  
Calculating the Variance of the Exponential Family  
The Variance Function  
 
4. Linear Structure and the Link Function
The Generalization  
Distributions  
 
5. Estimation Procedures
Estimation Techniques  
Profile Likelihood Confidence Intervals  
Comments on Estimation  
 
6. Residuals and Model Fit
Defining Residuals  
Measuring and Comparing Goodness-of-Fit  
Asymptotic Properties  
 
7. Extentions to Generalized Linear Models
Introduction to Extensions  
Quasi-Likelihood Estimation  
Generalized Linear Mixed Effects Model  
Fractional Regression Models  
The Tobit Model  
A Type-2 Tobit Model with Stochastic Censoring  
Zero Inflated Accomodating Models  
A Warning About Robust Standard Errors  
Summary  
 
8. Conclusion
Summary  
Related Topics  
Classic Reading  
Final Motivation  
 
9. References
Key features

NEW TO THIS EDITION:

  • New examples using real data include capital punishment (a count of executions in each state), electoral politics in Scotland (percentage of votes in favor of granting parliament taxation powers), and voting intention in the U.S. Republican presidential primaries (candidate selected from a list).
  • Strengthens material on the exponential family form, including a new discussion on multinomial distribution.
  • Adds more information on how to interpret results and make inferences in the chapter on estimation procedures.
  • A new section on extensions to generalized linear models.

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