Robust discriminant analysis using weighted likelihood estimators

Abstract

The procedures in traditional discriminant analysis suffer from serious lack of robustness under model misspecifications. Weighted likelihood estimators based on certain minimum divergence criteria have recently been shown (Markatou et al., 1998) to retain first order efficiency under the model while having attractive robustness properties away from it. In this paper, these estimators have been used to develop classifiers which are robust alternatives to Fisher's discriminant analysis. Results of an extensive simulation study and some real data sets are presented to illustrate the usefulness of the proposed methods.

^*E-mail: [email protected]

^†E-mail: [email protected]

Keywords:

• Discriminant analysis
• Hellinger distance
• Kernel density estimation
• Minimum divergence
• Robustness
• Smoothing
• Weighted likelihood estimators

Notes

^*E-mail: [email protected]

^†E-mail: [email protected]