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

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.