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Generalized Linear Models

Generalized Linear Models
A Unified Approach

Second Edition

May 2019 | 176 pages | SAGE Publications, Inc

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 the 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 ( as well as through the \texttt{R} package \texttt{GLMpack}. Supporting material (data and code) to replicate the examples in the book can be found in the 'GLMpack' package on CRAN or on the website

Series Editor's Introduction
About the Authors
1. Introduction
Model Specification

Prerequisites and Preliminaries

Looking Forward

2. The Exponential Family

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


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


8. Conclusion

Related Topics

Classic Reading

Final Motivation

9. References

Sample Materials & Chapters

2. The Exponential Family

For instructors

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