An Introduction to Generalized Linear
Models, Third Edition (Texts in
Statistical Science Series) by Adrian
Barnett
Clear Writing And Nice Examples
Continuing to emphasize numerical and graphical methods, An
Introduction to Generalized Linear Models, Third Edition provides a
cohesive framework for statistical modeling. This new edition of a
bestseller has been updated with Stata, R, and WinBUGS code as well as
three new chapters on Bayesian analysis. 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. It includes examples and exercises with complete data sets
for nearly all the models covered.
Personal Review: An Introduction to Generalized Linear Models,
Third Edition (Texts in Statistical Science Series) by Adrian
Barnett
Bill recommended Dobson's text because of her clear writing style and
many useful examples. Dobson also places the theory in the context of the
general exponential family of distributions. As I knew that the second
edition was about to come out I waited for it.
The wait seems to have been very worthwhile. The second edition is a real
bargin.... She has updated it with the many advances that have occurred
over the past 12 years since the first edition was printed. This edition now
includes some discussion of generalized additive models, broader
coverage of applications as survival analysis, GEE, multi-level models and
nominal and ordinal logistic regression have been added. It now offers the
reader more applications in a wider variety of disciplines and includes
modern approaches to diagnostic checking of the models.
As with the first edition, exploratory techniques are emphasized particularly
graphical methods. The goal is to unify the apparently disparate statistical
techniques that students are exposed to, into one general modeling
framework.
It includes a nice up-to-date bibliography and recent advanced results on
longitudinal models. The level is intermediate statistics with introductory
statistics and linear models taken to be prerequisites. Students are also
required to have some familiarity with calculus and linear algebra.
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