Do you have data that is not normally distributed and don′t know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to GLM (including Poisson regression. logistic regression, and proportional hazards models) and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets, and the computer instructions and results will be presented for each example. Throughout the book, there is an emphasis on link functions and error distribution and how the model specifications translate into likelihood functions that can, through maximum likelihood estimation be used to estimate the regression parameters and their associated standard errors. This book provides readers with basic modeling principles that are applicable to a wide variety of situations.
Key Features:
– Provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation
– Includes discussion on checking model adequacy and description on how to use SAS to fit GLM
– Describes the connection between survival analysis and GLM
This book is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.
Table des matières
List of Figures and Tables
Series Editor’s Introduction
Acknowledgments
1. Generalized Linear Models
2. Some Basic Modeling Concepts
Categorical Independent Variables
Essential Components of Regression Modeling
3. Classical Multiple Regression Model
Assumptions and Modeling Approach
Results of Regression Analysis
Multiple Correlation
Testing Hypotheses
4. Fundamentals of Generalized Linear Modeling
Exponential Family of Distributions
Classical Normal Regression
Logistic Regression
Poisson Regression
Proportional Hazards Survival Model
5. Maximum Likelihood Estimation
6. Deviance and Goodness of Fit
Using Deviances to Test Statistical Hypotheses
Goodness of Fit
Assessing Goodness of Fit by Residual Analysis
7. Logistic Regression
Example of Logistic Regression
8. Poisson Regression
Example of Poisson Regression Model
9. Survival Analysis
Survival Time Distributions
Exponential Survival Model
Example of Exponential Survival Model
Conclusions
Appendix
References
Index
About the Authors
A propos de l’auteur
Prof. Ho′s research concerns with the development and application of quantitative methods in the neural and behavioral sciences. His current research interests include effective connectivity analysis in f MRI experiments, social network analysis, statistical approach for testing mathematical axioms, diagnostics in nonlinear SEM.
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