Product Description

Updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis, this new edition of a bestseller continues to provide a cohesive framework for statistical modeling. Emphasizing numerical and graphical methods, it enables readers to understand the unifying structure that underpins GLMs. The text discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis. It contains numerous examples from business, medicine, engineering, and the social sciences and offers the data sets and solutions to the exercises online.

Product Details

• Amazon Sales Rank: #157768 in Books
• Published on: 2008-07-15
• Original language: English
• Number of items: 1
• Binding: Paperback
• 320 pages

From the Back Cover
Popular for its accessible, concise, and clear introduction to this key statistical methodology, An Introduction to Generalized Linear Models, Third Edition provides a wealth of examples from such diverse fields as business, medicine, engineering, and the social sciences. Emphasizing graphical methods for exploratory data analysis and visualization, this new edition offers more material on Bayesian methodology and additional advice on implementing methods using statistical software. It also has updated the examples and exercises and includes an appendix of selected solutions, enhancing its suitability for self-study.

Customer Reviews

An excellent introduction to GLM's
This book provides a short but very clear introduction to GLM's and applications. All major models are covered, providing a good survey of the wide application of these models and related techniques.
The text is mathematical in nature, but not extremely so.
The requirements are as far as I can see introduction courses in statistics, calculus and matrix algebra.
Almost all methods are illustrated with numerical examples. Data sets an solution outlines are available from the internet, making the book suitable for self study.
The only small point of critique is that I feel that the explanation of the numerical techniques used in the estimation of GLM's might be expanded (or additional material could be provided in appendix or on te internet).