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

~ (Author) "Discriminant analysis as a whole is concerned with the relationship between a categorical variable and a set of interrelated variables..." (more)
Key Phrases: homoscedastic normal model, unconditional error rates, overall apparent error rate, Monte Carlo, Das Gupta, Van Ness (more...)
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Customers buy this book with The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) by Robert Tibshirani

Discriminant Analysis and Statistical Pattern Recognition (Wiley Series in Probability and Statistics) + The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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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)


Product Description

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.

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Product Details

  • Paperback: 552 pages
  • Publisher: Wiley-Interscience (August 4, 2004)
  • Language: English
  • ISBN-10: 0471691151
  • ISBN-13: 978-0471691150
  • Product Dimensions: 8.9 x 6 x 1.1 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.com Sales Rank: #1,261,274 in Books (See Bestsellers in Books)

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More About the Author

Geoffrey J. McLachlan
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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First Sentence:
Discriminant analysis as a whole is concerned with the relationship between a categorical variable and a set of interrelated variables. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
homoscedastic normal model, unconditional error rates, overall apparent error rate, zero cutoff point, logistic discriminant rule, discrete feature data, heteroscedastic normal model, unclassified entities, unclassified entity, outright allocation, randomly chosen entity, mixture sampling scheme, posterior log odds, mixed feature variables, optimal discriminant rule, discriminant analysis context, sample linear discriminant function, best linear rule, estimative approach, typicality index, jth entity, classified training data, quadratic discriminant rule, classification likelihood approach, uniform covariance structure
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Das Gupta, Van Ness, Biometric Society
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33 of 33 people found the following review helpful:
5.0 out of 5 stars authoritative and very well-written, February 9, 2008
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.

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