On the posterior-probability estimate of the error rate ofnonparametric classification rules

The posterior-probability estimate of the classification error rate of some nonparametric classification rules is studied. The variance of the estimator is shown to have same remarkable distribution-free properties for the k-nearest neighbor, kernel, and histogram rules. We also investigate the bias of the estimate and establish its consistency and upper bounds. The version of the estimate calculated from an independent set of unclassified patterns is also considered