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Generalized Linear Models, Second Edition (Monographs on Statistics and Applied Probability)

Generalized Linear Models, Second Edition (Monographs on Statistics and Applied Probability)
By P. McCullagh, John A. Nelder

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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.


Product Details

  • Amazon Sales Rank: #243450 in Books
  • Published on: 1989-08-01
  • Original language: English
  • Number of items: 1
  • Binding: Hardcover
  • 532 pages

Editorial Reviews

Review
... 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.
-THES

... 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.
-Biometrics
... a definitive and unified presentation...by the outstanding experts of this field.
-Statistics
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.
-Technometrics


Customer Reviews

the book by the originators of the methodology5
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 time5
I got this book in time and in perfect condition. Prompt delivery!!!

first great treatment of generalized linear models5
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.