Generalized Linear Models, Second Edition (Monographs on Statistics and Applied Probability) | 
| Authors: P. Mccullagh, John A. Nelder
Publisher: Chapman & Hall/CRC
Category: Book
List Price: $104.95
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Avg. Customer Rating:  5 reviews
Sales Rank: 157528
Media: Hardcover
Edition: 2
Number Of Items: 1
Pages: 532
Shipping Weight (lbs): 1.9
Dimensions (in): 9.1 x 5.9 x 1.3
ISBN: 0412317605
Dewey Decimal Number: 519.5
EAN: 9780412317606
ASIN: 0412317605
Publication Date: August 1, 1989
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Product Description
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications.The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables.The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions.Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.
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| Customer Reviews:
the book by the originators of the methodology February 20, 2008
24 out of 24 found this review helpful
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.
As promised, on time March 21, 2006
0 out of 23 found this review helpful
I got this book in time and in perfect condition. Prompt delivery!!!
first great treatment of generalized linear models August 9, 2000
12 out of 17 found this review helpful
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.
Very comprehensive, very helpful. April 1, 2000
6 out of 16 found this review helpful
The first edition is already a well-known text and reference, this expanded version is even better. Very comprehensive and very helpful.
One of the best books on modelling March 31, 2000
41 out of 41 found this review helpful
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.
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