• List Price: $119.95
  • Save: $13.49 (11%)
In Stock.
Ships from and sold by Amazon.com.
Gift-wrap available.
Condition: Used: Good
Comment: Eligible for FREE Super Saving Shipping! Fast Amazon shipping plus a hassle free return policy mean your satisfaction is guaranteed! Good readable copy. Worn edges and covers and may have small creases. Otherwise item is in good condition.
Access codes and supplements are not guaranteed with used items.
Sell yours for a Gift Card
We'll buy it for $50.74
Learn More
Trade in now
Have one to sell? Sell on Amazon
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See all 2 images

Generalized Linear Models, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Hardcover – August 1, 1989

ISBN-13: 978-0412317606 ISBN-10: 0412317605 Edition: 2nd

Buy New
Price: $106.46
22 New from $100.00 21 Used from $92.50 1 Collectible from $214.67
Amazon Price New from Used from
"Please retry"
$100.00 $92.50
Unknown Binding
"Please retry"
Free Two-Day Shipping for College Students with Amazon Student Free%20Two-Day%20Shipping%20for%20College%20Students%20with%20Amazon%20Student

Frequently Bought Together

Generalized Linear Models, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) + An Introduction to Generalized Linear Models, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) + Categorical Data Analysis
Price for all three: $259.54

Buy the selected items together

Hero Quick Promo
Year-End Kindle Daily Deals
Load your library with great books for $2.99 or less each, today only. Learn more

Product Details

  • Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability (Book 37)
  • Hardcover: 532 pages
  • Publisher: Chapman and Hall/CRC; 2 edition (August 1, 1989)
  • Language: English
  • ISBN-10: 0412317605
  • ISBN-13: 978-0412317606
  • Product Dimensions: 9.1 x 6.3 x 1.3 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (6 customer reviews)
  • Amazon Best Sellers Rank: #164,306 in Books (See Top 100 in Books)

Editorial Reviews


... an important, useful book, well-written by two authorities in the field...
-Times Higher Education Supplement
... an enormous range of work is covered... represents, perhaps, the most important field of research in theoretical and practical statistics. For all statisticians working in this field, the book is essential.
-Short Book Reviews
... this is a rich book; rich in theory, rich in examples, and rich in a statistical sense. I highly recommend it.
... a definitive and unified presentation...by the outstanding experts of this field.
This is a wonderful book... Reading the book is like listening to a good lecturer. The authors present the material clearly, and they treat the reader with respect. There is a balance between discussion, mathematical presentation of models, and examples.

... a complete introduction to the topic in a single monograph... a very readable book that provides the reader with great insight into a vast array of data analysis techniques...
-Siam Review

... a unique and useful text for intermediate undergraduate teaching.

Customer Reviews

4.5 out of 5 stars
5 star
4 star
3 star
2 star
1 star
See all 6 customer reviews
Share your thoughts with other customers

Most Helpful Customer Reviews

50 of 51 people found the following review helpful By A Customer on March 31, 2000
Format: Hardcover
This is an important book. It is a mature, deep introduction to generalized linear models.

General linear models extend multiple linear models to include cases in which the distribution of the dependent variable is part of the exponential family and the expected value of the dependent variable is a function of the linear predictor. Besides the normal (Gaussian) distribution, the binomial distribution, the Poisson distribution and the Gamma distribution, are just some of the exponential family members most frequently encountered in the scientific literature. Using appropriate functions to join the dependent variable to the linear predictor many classic models of applied statistics are included in the broad frame of generalized linear models: "logistic regression", log-linear models, Cox's proportional hazards models are just some of them.

Further extensions to the "base" family of generalized linear models, such as those based on the use of quasi-likelihood functions, and models in which both the expected value and the dispersion are function of a linear predictor, are well presented in the book.

Examples, and exercises, introduce many non-banal, useful, designs.

There are some minor drawbacks. Some more advanced topics might have been introduced more smoothly (i.e. conditional likelihood). Some other topics are better understood when you are already familiar with the specific object of study (i.e. Cox's proportional hazards models as a generalized linear model). The book does not provide software examples, nor is it related with any specific statistical package. However, the maturity of the reader to whom the book is addressed should be so high that translating the majority of the examples presented in the book in the "language" of a familiar statistical package should not be a problem.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
31 of 32 people found the following review helpful By Michael R. Chernick on February 20, 2008
Format: Hardcover
Nelder and Wedderburn wrote the seminal paper on generalized linear models in the 1970s. Since then John Nelder has pioneered the research and software development of the methods. This is the first of several excellent texts on generalized linear models. It illustrates how through the use of a link function many classical statistical models can be unified into one general form of model. This unification is helpful both theoretically and computationally. Various applications are presented in a clear manner.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
1 of 1 people found the following review helpful By Chris on March 26, 2014
Format: Hardcover Verified Purchase
This was one of two books that were references for a course I took in Generalized Linear Models - GLM. This was an excellent book in explaining the technical (mathematical) details of the GLM, such as the importance of the link function (how covariates/predictors/explanatory variables are related to the response), the importance of moment generating functions and cumulant generating functions, and the contribution of various types of likelihoods (like conditional, quasi, and partial ) in parameter estimation in GLM. It also covers things like geometrical intepretations...which seems to be very important in the multivariate setting. Most of the problems in the book lean towards proofs. For example... problem 4.7 asks you to show that the ratio of a binomial and poisson random variable is asymptotically equivalent to a constant.

This book is kinda old, so things like Bayesian regression (which I'm not acquainted with...yet) or high dimensional data analysis are not going to be in this book. I know that the latest edition of Agresti's Categorical Data Analysis (CDA) does cover these topics though. Still, I think that the McCullagh book is more mathematically rigorous than Agresti's book, since it covers things like the geometrical interpretation of least squares estimation.

This book was clearly written for researchers who have a quantitative background - those who have a background in at least intermediate statistical theory (Casella & Berger) as well as statistical Linear Modeling. Anybody who only has some of this knowledge would probably find Agresti's Categorical Data Analysis more accessible (this was the other book used in our GLM course), and those don't have any math experience might find Agresti's An Introduction to Categorical Data Analysis a better book.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again