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Annette J. Dobson and Adrian G. Barnett: An introduction to generalized linear models : CRC Press, Boca Raton, FL, 2008, i + 307pp., US $ 59.95, € 57.99, ISBN 978-1-58488-950-2

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Stat Papers (2012) 53:805–806 DOI 10.1007/s00362-011-0375-4

B O O K R E V I E W

Annette J. Dobson and Adrian G. Barnett:

An introduction to generalized linear models

CRC Press, Boca Raton, FL, 2008, i + 307 pp., US $ 59.95,

e 57.99,

ISBN 978-1-58488-950-2

Christian Kleiber

Published online: 20 February 2011 © Springer-Verlag 2011

This is the 3rd edition of the well-known introductory text on generalized linear models (GLMs), now with a second author, A.G. Barnett.

Like the preceding edition the 3rd edition has been considerably expanded: the 2nd edition provided new chapters on nominal and ordinal regression, and also some survival analysis and analysis of longitudinal data, while the current edition adds three further chapters, all on Bayesian analysis and methodology. Specifically, there is now a chapter covering MCMC basics. A side effect of these numerous exten-sions is that the title is now somewhat misleading, in that not all models covered here are GLMs, although those that are not are, in a sense, close to GLMs and reuse GLM methodology. Examples include multinomial or ordinal logistic regression. A more appropriate title for the new edition would thus be “Introduction to Regression Modelling.”

Compared to earlier editions there is also more code showing how to use GLM methodology in various software packages, notably R and Stata; the Bayesian chap-ters employ a combination of R and WinBUGS. On occasion, the R code is somewhat uglier than need be.

Some 40 data sets are available from a Web page. Data and examples are mostly from biostatistics and related fields, thus the book will perhaps be less appealing to instructors in the social sciences.

Inevitably, discussion of some topics is rather brief. For example, the important issue of overdispersion in count data is only briefly mentioned, along with the nega-tive binomial distribution as one of its remedies. Thus instructors intending to cover count data in some detail might have to draw on additional sources.

C. Kleiber (

B

)

Universität Basel, Basel, Switzerland e-mail: christian.kleiber@unibas.ch

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806 C. Kleiber

Overall, this new edition remains a highly useful and compact introduction to a large number of seemingly disparate regression models. Depending on the back-ground of the audience, it will be suitable for upper level undergraduate or beginning post-graduate level courses.

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