Generalized Linear Models 2/e

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.

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