Introduction to General and Generalized Linear Models

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Part of the Chapman & Hall/CRC Texts in Statistical Science series

Since the mathematics behind generalized linear models is often difficult to follow while the mathematics behind general linear models is well understood, this text describes the methodology behind both models in a parallel setup. After introducing a likelihood framework sufficient to cover both approaches, the authors present general linear models, including analysis of covariance, before moving on to more complicated generalized linear models using the same example. Numerous simulated and real-world examples, implemented using R and SAS, illustrate the methods discussed. The text also provides exercises to further develop understanding.

Table of Contents

The Likelihood Principle. General Linear Models. Generalized Linear Models. Mixed Effects Models. Hierarchical Models. Some Probability Distributions.

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