James K. Lindsey

Applying Generalized Linear Models (eBook, PDF)

• Format: PDF

Produktbeschreibung

Generalized linear models have applications in many areas, including social science and life science. This book will serve as a reference and advanced text for students interested in the applications of statistics.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 256
• Erscheinungstermin: 15. Januar 2008
• Englisch
• ISBN-13: 9780387227306
• Artikelnr.: 43999684

Inhaltsangabe

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.

Rezensionen

From a review:

TECHNOMETRICS

"...one of the best applied statistical books that I have ever read."