Constrained linear discriminant rule via the Studentized classification statistic based on monotone missing data

Abstract

This paper provides an asymptotic expansion for the distribution of the Studentized linear discriminant function with k-step monotone missing training data. It turns out to be a certain generalization of the results derived by Anderson [1] and Shutoh and Seo [12]. Furthermore we also derive the cutoff point constrained by a conditional probability of misclassification using the idea of McLachlan [8]. Finally we perform Monte Carlo simulation to evaluate our results.