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Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Bias-corrected diagonal discriminant rules for high-dimensional classification

Language

English

Abstract

Diagonal discriminant rules have been successfully used for high-dimensional classification problems, but suffer from the serious drawback of biased discriminant scores. In this article, we propose improved diagonal discriminant rules with bias-corrected discriminant scores for high-dimensional classification. We show that the proposed discriminant scores dominate the standard ones under the quadratic loss function. Analytical results on why the bias-corrected rules can potentially improve the predication accuracy are also provided. Finally, we demonstrate the improvement of the proposed rules over the original ones through extensive simulation studies and real case studies. © 2010, The International Biometric Society.

Keywords

Bias correction, Diagonal discriminant analysis, Discriminant score, Large p small n, Tumor classification

Publication Date

2010

Source Publication Title

Biometrics

Volume

66

Issue

4

Start Page

1096

End Page

1106

Publisher

Wiley

ISSN (print)

0006341X

ISSN (electronic)

15410420

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