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An Introduction to Generalized Linear Models, Third Edition (Texts in Statistical Science Series) | 
 enlarge | Authors: Annette J. Dobson, Adrian Barnett
Publisher: Chapman & Hall/CRC
Category: Book
List Price: $59.95
Buy New: $45.65
You Save: $14.30 (24%)
New (18) from $45.65
Avg. Customer Rating:  8 reviews
Sales Rank: 369827
Media: Paperback
Edition: 3
Number Of Items: 1
Pages: 320
Shipping Weight (lbs): 1
Dimensions (in): 9.2 x 5.9 x 0.7
ISBN: 1584889500
Dewey Decimal Number: 519.5
EAN: 9781584889502
ASIN: 1584889500
Publication Date: May 12, 2008
Availability: Usually ships in 1-2 business days
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Product Description
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.
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| Customer Reviews: Read 3 more reviews...
 GLMs in a Nutshell February 23, 2008
4 out of 4 found this review helpful
If you are one of those people that like to learn few things and be able to apply them to many, this is a book for you. It provides derivations for properties of a whole family of distributions, which can be applied to each of the member distributions. It is short, sweet, and straight to the point. Basic knowledge of linear algebra and multivariate calculus might be necessary. As a complementary text and for a more detailed discussion, I would also recommend Statistical Models by David Freedman.
clear writing and nice examples January 23, 2008
20 out of 20 found this review helpful
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.
Annette BDobson book on GLM October 22, 2007
4 out of 4 found this review helpful
This book does exactly what it set out to do. It was recommended to me as as an excellent introduction to GLMs and in this it succeeds.
Even though it's not stated the book really assumes a knowledge of regression and basic ANOVA. If you havn't a reasonable knowledge of the basics of these, this book is not for you.
Armed with basic knowledge Annette Dobson's book is really good. The background theory is covered in the first 5 chapters. This is well structured and deals with the subject in a sensible manner and at a relatively quick pace. As such, it is ideally suited to the intermediate audience of a senior level lecture course and also to researchers who wish to quickly understand and use GLMs.
The second part of the book focuses on applications and interpretations with some more theory - this overlaps and uses the work of the earlier chapters. The key material is covered and the author quickly explains what the results mean and how they should be interpreted. Once again the exposition is thorough but brief and so it suited to a course work environment or the researcher doing self-study/refreshing their knowledge, but it's not for the novice or those starting out in statistical modelling.
 Clear and Consice but too Compact November 19, 2004
2 out of 4 found this review helpful
While what the book does explain about the statistical theory mentioned, it is too compact for what it tries to explain. There are also no answers to the excercises, which would be quite helpful given some of the questions asked. It's great for applications and is a good handbook, but for a thorough explanation of everything involved, I recomend getting a bigger textbook! For my 4th year Generalized Linear Models stats class, this book is helpful, but at times too compact to be more useful.
the most clearly written book on the topic August 16, 2002
6 out of 9 found this review helpful
My copy of the second edition just arrived yesterday and it is even better than the first edition (which was fantastic). The logical organization and clarity of writing make this book a 'must have' for any statistician's library. I'd give it 6 stars if I could. Readers should also check out McCulloch and Searle's 'Generalized, Linear and Mixed Models'.
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