Discriminant Analysis and Statistical Pattern Recognition (Wiley Series in Probability and Statistics) [Paperback]

Geoffrey J. McLachlan (Author)

Book Description

ISBN-10: 0471691151 | ISBN-13: 978-0471691150 | Publication Date: August 4, 2004

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"For both applied and theoretical statisticians as well as investigators working in the many areas in which relevant use can be made of discriminant techniques, this monograph provides a modern, comprehensive, and systematic account of discriminant analysis, with the focus on the more recent advances in the field."
–SciTech Book News

". . . a very useful source of information for any researcher working in discriminant analysis and pattern recognition."
–Computational Statistics

Discriminant Analysis and Statistical Pattern Recognition provides a systematic account of the subject. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule, and extensions of discriminant analysis motivated by problems in statistical image analysis. The accompanying bibliography contains over 1,200 references.

Editorial Reviews

Review

“ … in my opinion (this book) has been proved .. to be a valuable resource (and) should not be overlooked by any scholarly library.”  (Journal of the Royal Statistical Society Series A, June 2005)

From the Publisher

Provides a systematic account of the subject area, concentrating on the most recent advances in the field. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are: regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule and extensions of discriminant analysis motivated by problems in statistical image analysis. Includes over 1,200 references in the bibliography. --This text refers to an out of print or unavailable edition of this title.

See all Editorial Reviews

Product Details

• Paperback: 552 pages
• Publisher: Wiley-Interscience (August 4, 2004)
• Language: English
• ISBN-10: 0471691151
• ISBN-13: 978-0471691150
• Product Dimensions: 6 x 9 x 1.2 inches
• Shipping Weight: 1.7 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #2,138,034 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

30 of 30 people found the following review helpful:

5.0 out of 5 stars authoritative and very well-written, February 9, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Discriminant Analysis and Statistical Pattern Recognition (Hardcover)

Geoff McLachlan has written a thorough and up-to-date text on discriminant analysis and pattern recognition. There are a number of fine books on discriminant analysis. McLachlan's is one of the best. It is at an intermediate level and provides many of the recent advances including regularized discriminant analysis.
Discriminant analysis and pattern recognition are very similar topics. The term discriminant analysis is common in the statistical literature while pattern recognition is more common in the electrical engineering literature. McLachlan is scholarly and familiar with the literature in both disciplines (not common). He includes over 1200 references with many references from the late 1980s.

Professor McLachlan has been a key contributor to the literature on error rate estimation in discriminant analysis and devotes a great deal of coverage to this important topic. He also includes recent developments on bootstrap methods and summarizes the literature on bootstrap methods for adjusting bias in error rate estimation.

Much of the bootstrap work on error rate estimation involves comparative simulation studies, particular when training sample sizes are small. McLachlan provides a nice summary of the his work, the work of Efron and he also includes discussion of a couple of my simulation studies co-authored with Murthy and Nealy.