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

Generalized Linear Models: A Unified Approach: Quantitative Applications in the Social Sciences, cartea 134

Autor Jefferson M. Gill, Silvia Michelle Torres Pacheco
en Limba Engleză Paperback – 10 iul 2019
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.
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Specificații

ISBN-13: 9781506387345
ISBN-10: 1506387349
Pagini: 176
Dimensiuni: 140 x 216 x 20 mm
Greutate: 0.2 kg
Ediția:Revizuită
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences

Locul publicării:Thousand Oaks, United States

Cuprins

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

Notă biografică

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).

Descriere

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.