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Generalized Linear Models Jefferson M. Gill

Generalized Linear Models By Jefferson M. Gill

Generalized Linear Models by Jefferson M. Gill

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Summary

Explaining the theoretical underpinning of generalized linear models, this text enables researchers to decide how to select the best way to adapt their data for this type of analysis, with examples to illustrate the application of GLM.

Generalized Linear Models Summary

Generalized Linear Models: A Unified Approach by Jefferson M. Gill

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&


About Jefferson M. Gill

Jeff Gill is a Distinguished Professor of Government, a Professor of Statistics, and a Member of the Center for Behavioral Neuroscience at American University. His research applies Bayesian modeling and data analysis (decision theory, testing, model selection, elicited priors) to questions in general social science quantitative methodology, political behavior and institutions, medical/health data analysis especially physiology, circulation/blood, pediatric traumatic brain injury, and epidemiological measurement/data issues, using computationally intensive tools (Monte Carlo methods, MCMC, stochastic optimization, nonparametrics). Michelle Torres is Assistant professor in the Department of Political Science at Rice University. She holds a PhD in Political Science and a AM in Statistics from Washington University in St. Louis. Her research interests are in the fields of political methodology, with a special focus on survey methodology, computer vision, causal inference, public opinion, and political communication.

Table of Contents

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

Additional information

NPB9781506387345
9781506387345
1506387349
Generalized Linear Models: A Unified Approach by Jefferson M. Gill
Jefferson M. Gill
Quantitative Applications in the Social Sciences
New
Paperback
SAGE Publications Inc
2019-07-11
176
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
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