An asymptotic approximation for EPMC in linear discriminant analysis based on monotone missing data

In this paper, we propose an asymptotic approximation for the expected probabilities of misclassification (EPMC) in the linear discriminant function on the basis of k-step monotone missing training data for general k. We derive certain relations of the statistics in order to obtain the approximation. Finally, we perform Monte Carlo simulation to evaluate the accuracy of our result and to compare it with existing approximations.

► We embed monotone missing data into discrimination scheme.
► We consider an approximation for the expected probabilities of misclassification.
► The useful relations between the estimates derive an approximation.
► Our approximation turns out to be an extension for the existing approximation.