Optimal asymmetric classification procedures for interval-screened normal data

Statistical methods for an asymmetric normal classification do not adapt well to the situations where the population distributions are perturbed by an interval-screening scheme. This paper explores methods for providing an optimal classification of future samples in this situation. The properties of the screened population distributions are considered and two optimal regions for classifying the future samples are obtained. These developments yield yet other rules for the interval-screened asymmetric normal classification. The rules are studied from several aspects such as the probability of misclassification, robustness, and estimation of the rules. The investigation of the performance of the rules as well as the illustration of the screened classification idea, using two numerical examples, is also considered.