Department of Mathematics
Bias-corrected diagonal discriminant rules for high-dimensional classification
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.
Bias correction, Diagonal discriminant analysis, Discriminant score, Large p small n, Tumor classification
Source Publication Title
Huang, Song, Tiejun Tong, and Hongyu Zhao. "Bias-corrected diagonal discriminant rules for high-dimensional classification." Biometrics 66.4 (2010): 1096-1106.