![Applying Generalized Linear Models]( "Applying Generalized Linear Models")
Click to have a closer look
About this book
Contents
Customer reviews
Related titles
About this book
Provides a valuable overview of linear, categorical and survival models and shows that they have much in common. The reader is assumed to have knowledge of basic statistical principles and should be familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.
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.
Customer Reviews
