Generalized Linear Models: A Unified Approach

Generalized Linear Models: A Unified Approach

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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. Authors Jeff Gill and Michelle Torres provide 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.

Product Details

ISBN-13: 9781506387345
Publisher: SAGE Publications
Publication date: 06/17/2019
Series: Quantitative Applications in the Social Sciences , #134
Edition description: Second Edition
Pages: 176
Sales rank: 859,715
Product dimensions: 5.50(w) x 8.50(h) x (d)

About the Author

Jeff Gill is Distinguished Professor in the Department of Government, Professor in the Depart- ment of Mathematics & Statistics, and a Member of the Center for Behavioral Neuroscience at American University. He is also the inaugural director of the Center for Data Science and Co-Director of the graduate program in Data Science there. In additional to theoretical and methodological work in Bayesian statistics and statistical computing, his applied work centers on studying human beings from social, political, and biomedical perspectives.

Michelle Torres is Assistant Professor in the Department of Political Science at Rice University. Her core research covers political methodology, specifically survey methodology, computer vision, and causal inference. Substantively, she focuses on public opinion, participation, and psycholog- ical traits. She holds a Ph.D. in Political Science and an A.M. in Statistics from Washington University in St. Louis.

Table of Contents

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

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