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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd Edition

4.2 out of 5 stars 9 customer reviews
ISBN-13: 978-0387953649
ISBN-10: 0387953647
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Editorial Reviews

About the Author

Burnham of the Colorado Cooperative Fish & Wildlife Research Unit, Ft. Collins

Anderson received B.S. and M.S. degrees in wildlife biology an a PhD in theoretical ecology. He currently is Senior Scientist ith the Biological Resources Division within the U.S Geological Survey and a professor in the Department of Fishery and Wildlife Biology.
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Product Details

  • Hardcover: 488 pages
  • Publisher: Springer; 2nd edition (December 4, 2003)
  • Language: English
  • ISBN-10: 0387953647
  • ISBN-13: 978-0387953649
  • Product Dimensions: 6.1 x 1.1 x 9.2 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (9 customer reviews)
  • Amazon Best Sellers Rank: #884,107 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Michael R. Chernick on February 9, 2008
Format: 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.
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Format: Hardcover Verified Purchase
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.
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Format: Paperback
This book emphasizes the fundamentals of proper model selection that are pretty hard to master in school. Building a good model is a long process that requires a decent level of qualitative understanding of the data generating mechanism. Based on that knowledge, a sizable amount of work should be done well before the data are plugged into a statistical software package. Of course, if one is in a very data-rich situation, one can get away with "let the computer sort it out" approach, but such cases are rare.

After I finished the book, my understanding of the bias-variance tradeoff principle improved substantially. In particular, one should remember that overfitting is not only about including redundant covariates that then cause abysmal out-of-sample performance. Suppose, based on the qualitative information about the data generating process, you are convinced that certain factor(s) "should" be in the model. As the sample size decreases, you may find out that the "compulsory" factor(s) must be dropped to preserve the optimal bias-variance tradeoff. The catch is that, even if you manage to guess exactly what factors constitute the "true" model, the corresponding regression coefficients still have to be estimated from the data. The more coefficients, the greater the complexity of your model pool. If the sample size is low enough, you will be forced to reduce the complexity to avoid overfitting, which entails excluding some "true" factors from consideration.

Another major statistical concept the book clarified for me is the elusive distinction between "fixed" and "random" effects.
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Format: Paperback Verified Purchase
This is an excellent book on model selection and multi-model inference. It covers in great detail the underlying theoretical and philosophical foundations for model selection and provides practical examples with sufficient degree of detail so you could replicate the results on your own. I found it especially useful that this book applies the Aikake Information Criteria to non-stationary time series, as in 3.3 Example 2: Time Distribution of an Insecticide Added to a Simulated Ecosystem.
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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.
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