Applying Generalized Linear Models

• Main Author: Lindsey, James K. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York, 1997.
• Series: Springer Texts in Statistics,
• Subjects: Mathematics.; Probabilities.; Statistics.; Probability Theory and Stochastic Processes.; Statistical Theory and Methods.; Statistics for Life Sciences, Medicine, Health Sciences.

Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview...

Table of Contents:

• Generalized Linear Modelling: Statistical Modelling
• Exponential Dispersion Models
• Linear Structure
• Three Components of a GLM
• Possible Models
• Inference
• Exercises. Discrete Data: Log Linear Models
• Models of Change
• Overdispersion
• Exercises. Fitting and Comparing Probability Distributions: Fitting Distributions
• Setting Up the Model
• Special Cases
• Exercises. Growth Curves: Exponential Growth Curves
• Logistic Growth Curve
• Gomperz Growth Curve
• More Complex Models
• Exercises. Time Series: Poisson Processes
• Markov Processes
• Repeated Measurements
• Exercises. Survival Data: General Concepts
• 'Nonparametric' Estimation
• Parametric Models
• 'Semiparametric' Models
• Exercises. Event Histories: Event Histories and Survival Distributions
• Counting processes
• Modelling Event Histories
• Generalizations
• Exercises. Spatial data: Spatial Interaction
• Spatial Patterns
• Exercises. Normal Models: Linear Regression
• Analysis of Variance
• Nonlinear Regression
• Exercises. Dynamic Models: Dynamic Generalized Linear Models
• Normal Models
• Count Data
• Positive Response Data
• Continuous Time Nonlinear Models. Appendices: Inference
• Diagnostics
• References
• Index.