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Publisher Comments:
This book 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 student, 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, and of their unity. This book should appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With the many exercises at the end of the chapters, it should constitute an excellent text for teaching applied statistics students and non-statistics majors 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, being 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 Liége, and in medical statistics at DeMontfort University, Leiccster. He is the author of eight other books.
Description:
Includes bibliographical references (p. [231]-242) abd index.
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Table of Contents
1. Generalized Linear modelling: 1.1 Statistical modelling: 1.2 Exponential dispersion models: 1.3 Linear structure: 1.4 Three components of a GLM: 1.5 Possible models: 1.6 Inference: 1.7 Exercises: 2. Discrete data: 2.1 Log linear models: 2.2 Models of change: 2.3 Overdispersion: 2.4 Exercise: 3. Fitting and comparing probability distributions: 3.1 Fitting distribution: 3.2 Setting up the model: 3.3 Special cases: 3.4 Exercises: 4. Growth curves: 4.1 Exponential growth curves: 4.2 Logistic growth curve: 4.3 Company growth curve: 4.4 More complex models: 4.5 Exercises: 5. Time series: 5.1 Poisson processes: 5.2 Markov processes: 5.3 Repeated measurements: 5.4 Exercises: 6. Survival data: 6.1 General concepts: 6.2 Non-parametric estimation: 6.3 Parametric models: 6.4 Semi-parametric models: 6.5 Exercises: 7. Event histories: 7.1 Event histories and survival distributions: 7.2 Counting pocesses: 7.3 Modelling event histories: 7.4 Generalizations: 7.5 Exercises: 8. Spatial data: 8.1 Spatial interaction: 8.2 Spatial patterns: 8.3 Exercises: 9. Normal linear models: 9.1 Linear regression: 9.2 Analysis of variance: 9.3 Non-linear regression: 9.4 Exercises: 10. Dynamic models: 10.1 Dynamic generalized linear models: 10.2 Normal models: 10.3 Count data: 10.4 Positive response data: 10.5 continuous time non-linear models: A Inference: A.1 Direct likelihood inference: A.2 Frequentist decision-making: A.3 Bayesian decision-making: B Diagnostics: B.1 Model checking: B.2 Residuals: B.3 Isolated departures: B.4 Systematic departures
Product Details
ISBN:
9780387982182
Author:
Lindsey, James K.
Publisher:
Springer
Author:
Lindsey, James K.
Location:
New York :
Subject:
Statistics
Subject:
Probability
Subject:
Linear models (statistics)
Subject:
Linear models.
Subject:
Probability & Statistics - General
Copyright:
1997
Edition Description:
1997. Corr. 3rd
Series:
Springer Texts in Statistics
Series Volume:
1321
Publication Date:
June 1997
Binding:
Hardcover
Language:
English
Illustrations:
Yes
Pages:
276
Dimensions:
9.52x6.37x.71 in. 1.17 lbs.
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