Generalized Linear Models

ISBN-10: 0205377939

ISBN-13: 9780205377930

Edition: 2004

Authors: John P. Hoffmann
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Book details

List price: $115.40
Copyright year: 2004
Publisher: Allyn & Bacon, Incorporated
Publication date: 8/15/2003
Binding: Paperback
Pages: 216
Size: 6.90" wide x 8.90" long x 0.70" tall
Weight: 1.012
Language: English

Each chapter concludes with Conclusion and Exercises
Introduction
A Review of the Linear Regression Model
Issues of Interest
How to Estimate a Linear Regression Model
A Detailed Example of an OLS Regression Model
The Assumptions of the OLS (Linear) Regression Model
Interaction Terms in the OLS (Linear) Regression Model
Introduction to Generalized Linear Models
The Role of the Link Function
The Binomial Distribution
The Multinomial Distribution
The Poisson Distribution
The Negative Binomial Distribution
How Do We Estimate Regression Models Based on These Distributions? How to Check the Significance of Coefficients and the Fit of the Model
Logistic and Probit Regression Models
What Are the Alternatives to the Linear Regression Model? Diagnostic Tests for the Logistic Regression Model
Ordered Logistic and Probit Regression Models
Alternative Models for Ordinal Dependent Variables
The Ordered Logistic Regression Model
Testing the Proportional Odds Assumption
The Ordered Probit Regression Model
Introducing Multiple Independent Variables
The Multinomial Logistic Regression Model
Introducing Multiple Independent Variables
Diagnostic Tests for the Multinomial Logistic Regression Model
Alternatives to the Multinomial Logistic Regression Model
Poisson and Negative Binomial Regression Models
The Poisson Regression Model
The Overdispersed Poisson Regression Model
The Negative Binomial Regression Model
Diagnostic Tests for the Poisson Regression Model
Other Models for Count Variables
Event History and Survival Models
Continuous versus Discrete Time Models
Censoring and Time-Dependent covariates
The Basics: Survivor and Hazard Functions and Curves
Parametric Event History Models
The Cox Proportional Hazards Model
Where Do We Go from Here?
Sample Selection
Endogeneity
Longitudinal Data
Multilevel Models
Nonparametric regression
Stata, SPSS, and SAS Programs for Examples in Chapters
References
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