Average Reviews:
(More customer reviews)This book describes techniques for finding good predictors of some phenomenon (assuming linear dependence). For example, suppose you want to know what factor determine IQ. You do a survey that includes 500 factors such as "family income", "length of hair", "father's IQ", etc. Now determine which are the 20 best determinants of IQ! Thats impossible (practically speaking) because you'd have to do an exhaustive search of 500 choose 20 variables which would take to much computing power. The book describes known techniques which identify factors that are good predictors (but not necessarily the best).
In my searches, I found this book to be the best available on the subject. I found it difficult to read -- it is math heavy with emphasis on linear algebra.
The author is certainly one of the most authoritative subject matter experts. He supports free fortran source code which are available on his WEB site. He also maintains software (for MATHLAB I think) which use the techniques.
I would like the book to be targetted at three levels of audiences (I know - I want to world!). The book should include the math - for the theoretician. The book should be organized so persons not interested in the theory can figure out what the techiques do (at a high level). Finally, the book should identify which methods are supported by the major commerical statistical packages (e.g., SPSS, MATLAB, etc).
Click Here to see more reviews about: Subset Selection in Regression, Second Editon
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:"A separate chapter on Bayesian methods"Complete revision of the chapter on estimation"A major example from the field of near infrared spectroscopy"More emphasis on cross-validation"Greater focus on bootstrapping"Stochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible "Software available on the Internet for implementing many of the algorithms presented"More examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting and choosing models that are linear in their parameters and to understanding and correcting the bias introduced by selecting a model that fits only slightly better than others. The presentation is clear, concise, and belongs on the shelf of anyone researching, using, or teaching subset selecting techniques.
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