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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… Click to show full abstract
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 p ∕ n approaches a positive constant γ . In addition, we compare its misclassification rate to the standard D-rule under various settings via simulation.
Journal Title: Journal of The Korean Statistical Society
Year Published: 2018
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