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Generalized linear models and extensions
"The third edition of Generalized Linear Models and Extensions provides a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorithm that allow modeling of data that violate GLM distributional assumptions. The text stands out in its coverage of the derivation of GLM families and of their foremost links, and also guides the reader in how to apply the various GLM and GLM-extensions to real data. This edition has added a new chapter on data synthesis, which provides instruction on simulating independent as well as correlated data. Regression models illustrated with synthetic and real data are provided throughout the book to enable readers to better understand the models and their assumptions. We have also added discussion of models such as Poisson-inverse Gaussian, generalized Poisson, and generalized negative...
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