- 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: 6 x 1.2 x 9.2 inches
- Shipping Weight: 1.8 pounds (View shipping rates and policies)
- Average Customer Review: 9 customer reviews
- Amazon Best Sellers Rank: #506,733 in Books (See Top 100 in Books)
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Generalized Linear Models, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) 2nd Edition
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..." 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 ..." 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
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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.
As a learning text, however, the book has some deficiencies. GLM beginners probably want to know answers to question like:
1) Why should I perform GLM rather than OLS?
2) How do I determine an appropriate variance and link function?
3) How can I test whether my GLM has outperformed OLS?
As is, the reader has to read through some 500 pages of theory to find answers to these relatively simple questions. There is real room for a text that could provide an easier approach to mastering this material.
Even so, the book succeeds in its aim to provide an authoritative guide to GLM theory and practice. It deserves a place on every applied statistician's bookshelf.
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.