by James W. Hardin (2018)
The fourth edition of Generalized Linear Models and Extensions gives 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 the GLM families and their foremost links, but it also guides the reader in applying the various models to real data. This edition has new sections on bivariate and multivariate models including bivariate count data models estimated via copula functions and models based on bivariate distributions put forward by Famoye and by Marshall and Olkin. In addition, there are new sections on Bayesian GLMs illustrating background, the estimation of models using the bayesmh command of Stata 14, and the updated bayes prefix syntax available in Stata 15.
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by Julian J. Faraway (2005)
Like 'Generalized Linear Models and Extensions', this book offers deep dives into statistical modeling and R implementation.
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This book, like 'Generalized Linear Models and Extensions', focuses on practical regression analysis with a Bayesian perspective.
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Similar to 'Generalized Linear Models and Extensions', this text provides a deep theoretical foundation in statistical methods.
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This book shares with 'Generalized Linear Models and Extensions' a focus on specific statistical modeling techniques and their applications.
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This book, much like 'Generalized Linear Models and Extensions', offers comprehensive coverage of statistical modeling principles.
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