Model Selection and Multi-Model Inference [Hardcover]

Kenneth P. Burnham (Author), David Anderson (Author)

Book Description

ISBN-10: 0387953647 | ISBN-13: 978-0387953649 | Publication Date: July 12, 2002 | Edition: 2nd

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

Product Details

• Hardcover: 496 pages
• Publisher: Springer; 2nd edition (July 12, 2002)
• Language: English
• ISBN-10: 0387953647
• ISBN-13: 978-0387953649
• Product Dimensions: 9.2 x 6 x 0.9 inches
• Shipping Weight: 1.8 pounds (View shipping rates and policies)
• Average Customer Review: 4.4 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #793,079 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

33 of 34 people found the following review helpful:

4.0 out of 5 stars excellent book on model selection, February 9, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
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This review is from: Model Selection and Inference: A Practical Information-Theoretic Approach (Hardcover)

Burnham and Anderson have put together a scholarly account of the developments in model selection techniques from the information theoretic viewpoint. This is an important practical subject. As computer algorithms become more and more available for fitting models and data mining and exploratory analysis become more popular and used more by novices, problems with overfitting models will again raise their ugly heads. This has been an issue for statisticians for decades. But the problems and the art of model selection has not been commonly covered in elementary courses on statistics and regression. George Box puts proper emphasis on the iterative nature of model selection and the importance of applying the principle of parismony in many of his books. Classic texts on regression like Draper and Smith point out the pitfalls of goodness of ift measures like R-square and explain Mallows Cp and adjusted R-square. There are now also a few good books devoted to model selection including the book by McQuarrie and Tsai (that I recently reviewed for Amazon) and the Chapman and Hall monograph by A. J. Miller.
Burnham and Anderson address all these issues and provide the best coverage to date on bootstrap and cross-validation approaches. They also are careful in their historical account and in putting together some coherence to the scattered literature. They are thorough in their references to the literature. Their theme is the information theoretic measures based on the Kullback-Liebler distance measure. The breakthrough in this theory came from Akaike in the 1970s and improvements and refinement came later. The authors provide the theory, but more importantly, they provide many real examples to illustrate the problems and show how the methods work.

They also refer to the recent work in Bayesian methods. Chapter 1 is a great introduction that everyone should read. Being a fan of the bootstrap I was interested in their coverage of it in chapters 4, 5 and 6 (much of which is the authors' own work).

Because the authors work in biological fields they cover survival models as well as the standard time series and regression models where most of the emphasis has been placed on model selection in the past.

It is a great reference source and an important book for learning about model selection as part of the inferential process. The pictures of the famous contributors inserted throughout the book is also nice to see. We have Akaike, Boltzmann, Shibata, Kullback, and Liebler brought to life in photographs or sketches.

24 of 25 people found the following review helpful:

4.0 out of 5 stars Good, but far too prolix, August 23, 2005

By 

Neil Frazer (Kailua, HI United States) - See all my reviews
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This review is from: Model Selection and Multi-Model Inference (Hardcover)

I admire this book very much for its accessible treatment of AIC, but if were reduced in length by half, it would be twice as good. The authors cannot resist repeating themselves, usually several times, especially when giving advice of the "motherhood and apple pie" variety. Another annoying feature is that many references are given for philosophical points, yet sometimes when a useful result is given without proof, no reference is provided. For example, on page 12 an expression for maximized likelihood is given without a derivation or a reference. Inside this fat book there is a thin book crying to be let out.

4 of 4 people found the following review helpful:

4.0 out of 5 stars Critical book, somewhat difficult, October 7, 2010

By 

Eli M Swanson (Michigan State University) - See all my reviews

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This review is from: Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach (Paperback)

If you want to learn about model selection techniques and multimodel inference, this is your book. In my opinion, the first few chapters should be required reading for anyone using model selection techniques. The later chapters become quite technical (above my head, I'm not ashamed to say!) but they are undoubtedly important as well, and I'll work through them eventually merely due to the merit I find in the chapters I have read.