Title: An introduction to generalized linear models /
Authors: Dunteman, George H. 1935-; Ho, Moon-Ho R.
Publisher: Sage Publications,
Year: 2006
Subjects: Regression analysis Mathematical models.; Linear models (Statistics); Regression Analysis; Linear Models
Abstract: "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."--pub. desc.
Contents: 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.