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Subset Selection in Regression
Contributor(s): Miller, Alan (Author)
ISBN: 1584881712     ISBN-13: 9781584881711
Publisher: CRC Press
OUR PRICE:   $209.00  
Product Type: Hardcover - Other Formats
Published: April 2002
Qty:
Annotation: Originally published in 1990, Subset Selection in Regression filled a gap in the literature. Its critical and popular success has continued for more than a decade, and the second edition promises to continue that tradition. The author has thoroughly updated each chapter, added material that reflects developments in theory and methods, and included more examples and recent references. His treatment now includes a new chapter on Bayesian methods, greater emphasis on least-squares projections, and more material on cross-validation. The presentation is clear, concise, and as the Journal of the ASA reported about the first edition, goes "straight to the guts of a complex problem."
Additional Information
BISAC Categories:
- Science
- Mathematics | Probability & Statistics - General
Dewey: 519.536
LCCN: 2002020214
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Prob
Physical Information: 0.77" H x 6.54" W x 9.5" (1.05 lbs) 256 pages
 
Descriptions, Reviews, Etc.
Publisher Description:

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 examples

    Subset 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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