Accuracy of regularized D-rule for binary classification

We consider a regularized D-classification rule for high dimensional binary classification, which adapts the linear shrinkage estimator of a covariance matrix as an alternative to the sample covariance matrix in the D-classification rule (D-rule in short). We find an asymptotic expression for misclassification rate of the regularized D-rule, when the sample size $n$ and the dimension $p$ both increase and their ratio $p∕n$ approaches a positive constant $\gamma$. In addition, we compare its misclassification rate to the standard D-rule under various settings via simulation.