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‘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.’
-Sci Tech 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.
Table of Content
Preface.
1. General Introduction.
2. Likelihood-Based Approaches to Discrimination.
3. Discrimination via Normal Models.
4. Distributional Results for Discrimination via Normal
Models.
5. Some Practical Aspects and Variants of Normal Theory-Based
Discriminant Rules.
6. Data Analytic Considerations with Normal Theory-Based
Discriminant Analysis.
7. Parametric Discrimination via Nonnormal Models.
8. Logistic Discrimination.
9. Nonparametric Discrimination.
10. Estimation of Error Rates.
11. Assessing the Reliability of the Estimated Posterior
Probabilities of Group Membership.
12. Selection of Feature Variables in Discriminan Analysis.
13. Statistical Image Analysis.
References.
Author Index.
Subject Index.
About the author
Geoffrey J. Mc Lachlan, Ph D, is Professor of Mathematics at the University of Queensland, Australia. He is the author, with David Peel, of Finite Mixture Models(Wiley) and, with Thriyambakam Krishnan, of The EM Algorithm and Extensions(Wiley), among others.
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