Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7546091 | Journal of the Korean Statistical Society | 2018 | 11 Pages |
Abstract
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 pân approaches a positive constant γ. In addition, we compare its misclassification rate to the standard D-rule under various settings via simulation.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Won Son, Johan Lim, Xinlei Wang,