An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.
Nearly 20 years since I learnt about GLMs with the first edition of this as a text. I just re-read it and while naturally dated with regard to software and recent developments (regularisation etc) it is still an excellent, clear introduction. I haven't seen recent editions but am sure they are good too. Some linear algebra and calculus required.
An entry level for generalized linear models. The books covers all the essential things you should know about GLM but ignores many necessary details for the beginners. It is a good choice for beginners who want a quick survey about GLM.