Discriminant Analysis and Statistical Pattern Recognition

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.