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Model Selection and Multi-Model Inference
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Binding: Hardcover
Dewey Decimal Number: 570.151
EAN: 9780387953649
Edition: 2nd
ISBN: 0387953647
Label: Springer
Manufacturer: Springer
Number Of Items: 1
Number Of Pages: 496
Publication Date: December 04, 2003
Publisher: Springer
Studio: Springer
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Editorial Review:
The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference). A philosophy is presented for model-based data analysis and a general strategy outlined for the analysis of empirical data. The book invites increased attention on a priori science hypotheses and modeling. Kullback-Leibler Information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection. The maximized log-likelihood function can be bias-corrected as an estimator of expected, relative Kullback-Leibler information. This leads to Akaike's Information Criterion (AIC) and various extensions. These methods are relatively simple and easy to use in practice, but based on deep statistical theory. The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are objective and practical to employ across a very wide class of empirical problems. The book presents several new ways to incorporate model selection uncertainty into parameter estimates and estimates of precision. An array of challenging examples is given to illustrate various technical issues. This is an applied book written primarily for biologists and statisticians wanting to make inferences from multiple models and is suitable as a graduate text or as a reference for professional analysts.
Customer Reviews
Average Rating: 
Rating:  - excellent book on model selection
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 ... Read More
Rating:  - Model Selection and Multi-Model Inference
Those interested in mark-recapture models definitely should have this extraordinary book.
Very complete and easy to read
Rating: - Good, but far too prolix
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 ... Read More
Rating: - One of the best introduction to AIC (Akaike's Information Criterion)!!!
AIC is one of the widely known methods in model selection and inference.
This book includes not only a basic use but also advanced issues of the information-theoretic approach.
Using this book, you will learn the application of AIC soon!
Rating: - authoritative and thorough treatment
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 ... Read More
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