Optimizing Allocation Rules in Discrete and Continuous Discriminant Analysis

This paper presents an approach for the study of probabilistic outcomes in experiments with multiple possible results. An approach to obtain confidence ellipsoids for the vector of probabilities, which represents the likelihood of specific results, for both discrete and continuous discriminant analysis, is presented. The obtention of optimal allocation rules, in order to reduce the allocation costs is investigated. In the context of discrete discriminant analysis, the approach focuses on assigning elements to specific groups in an optimal way. Whereas in the continuous case, the approach involves determining the regions where each action is the optimal choice. The effectiveness of the proposed approach is examined with two numerical applications. One of them uses real data, while the other one uses simulated data.