Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related models. Thoroughly revised and updated, An Introduction to Generalized Linear Models, Second Edition continues to initiate intermediate students of statistics, and the many other disciplines that use statistics, in the practical use of these models and methods. The new edition incorporates many of the important developments of the last decade, including survival analysis, nominal and ordinal logistic regression, generalized estimating equations, and multi-level models. It also includes modern methods for checking model adequacy and examples from an even wider range of application. Statistics can appear to the uninitiated as a collection of unrelated tools. An Introduction to Generalized Linear Models, Second Edition illustrates how these apparently disparate methods are examples or special cases of a conceptually simple structure based on the exponential family of distribution, maximum likelihood estimation, and the principles of statistical modelling.
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INTRODUCTION 
Background 
Scope 
Notation 
Distributions Related to the Normal Distribution 
Quadratic Forms 
Estimation 
Exercises 
MODEL FITTING 
Introduction 
Examples 
Some Principles of Statistical Modelling 
Notation and Coding for Explanatory Variables 
Exercises 
EXPONENTIAL FAMILY AND GENERALIZED LINEAR 
MODELS 
Introduction 
Exponential Family of Distributions 
Properties of Distributions in the Exponential Family 
Generalized Linear Models 
Examples 
Exercises 
ESTIMATION 
Introduction 
Example: Failure Times for Pressure Vessels 
Maximum Likelihood Estimation 
Poisson Regression Example 
Exercises 
INFERENCE 
Introduction 
Sampling Distribution for Score Statistics 
Taylor Series Approximations 
Sampling Distribution for Maximum Likelihood Estimators 
Log-Likelihood Ratio Statistic 
Sampling Distribution for the Deviance 
Hypothesis Testing 
Exercises 
NORMAL LINEAR MODELS 
Introduction 
Basic Results 
Multiple Linear Regression 
Analysis of Variance 
Analysis of Covariance 
General Linear Models 
Exercises 
BINARY VARIABLES AND LOGISTIC REGRESSION 
Probability Distributions 
Generalized Linear Models 
Dose Response Models 
General Logistic Regression Model 
Goodness of Fit Statistics 
Residuals 
Other Diagnostics 
Example: Senility and WAIS 
Exercises 
NOMINAL AND ORDINAL LOGISTIC REGRESSION 
Introduction 
Multinominal Distribution 
Nominal Logistic Regression 
Ordinal Logistic Regression 
General Comments 
Exercises 
COUNT DATA, POISSON REGRESSION, AND LOG-LINEAR MODELS 
Introduction 
Poisson Regression 
Examples of Contingency Tables 
Probability Models for Contingency Tables 
Log-Linear Models 
Inference for Log-Linear Models 
Numerical Examples 
Remarks 
Exercises 
SURVIVAL ANALYSIS 
Introduction 
Survivor Functions and Hazard Functions 
Empirical Survivor Function 
Estimation 
Inference 
Model checking 
Example: Remission Times 
Exercises 
clustered and longitudinal data 
Introduction 
Example: Recovery from Stroke 
Repeated Measures Models for Normal Data 
Repeated Measures Models for NON-NORMAL DATA 
Multilevel Models 
Stroke Example Continued 
Comments 
Exercises 
SOFTWARE 
REFERENCES 
INDEX
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