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A Non-Parametric Variable Selection Algorithm for Allocatory Linear Discriminant Analysis

A computationally efficient nonparametric algorithm is proposed for the selection of variables in allocatory discriminant analysis. The efficiency of the algorithm derives from an ability to reuse calculations for the inverse of a nonsingular matrix. A subset of the original variables is found for which the leave-one-out estimate of the conditional probability of misclassification is never significantly greater than the estimated conditional probability of misclassification for the full set of predicate variables.