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.

Geoffrey J. McLachlan, PhD, 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.

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.