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Applying Generalized Linear Models : Springer Texts in Statistics - James K. Lindsey

Applying Generalized Linear Models

Springer Texts in Statistics


Published: 20th April 2000
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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 whereby they can see that the three areas - linear normal, categorical, and survival models - have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression and ANOVA. The author is professor in the biostatistics department at Limburgs University, Diepenbeek, in the social science department at the University of Liege, and in medical statistics at DeMontfort University, Leicester. He is the author of nine other books.

From a review:


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

Generalized Linear Modelling: Statistical Modelling
Exponential Dispersion Models
Linear Structure
Three Components of a GLM
Possible Models
Discrete Data
Log Linear Models
Models of Change
Fitting and Comparing Probability Distributions
Fitting Distributions
Setting Up the Model
Special Cases
Growth Curves
Exponential Growth Curves
Logistic Growth Curve
Gomperz Growth Curve
More Complex Models
Time Series
Poisson Processes
Markov Processes
Repeated Measurements
Survival Data
General Concepts
"Nonparametric" Estimation
Parametric Models
"Semiparametric" Models
Event Histories
Event Histories and Survival Distributions
Counting processes
Modelling Event Histories
Spatial data
Spatial Interaction
Spatial Patterns
Normal Models
Linear Regression
Analysis of Variance
Nonlinear Regression
Dynamic Models
Dynamic Generalized Linear Models
Normal Models
Count Data
Positive Response Data
Continuous Time Nonlinear Models
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9780387982182
ISBN-10: 0387982183
Series: Springer Texts in Statistics
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 256
Published: 20th April 2000
Publisher: Springer-Verlag New York Inc.
Country of Publication: US
Dimensions (cm): 24.13 x 16.51  x 1.91
Weight (kg): 0.52