Bias-Corrected Diagonal Discriminant Rules for High-Dimensional Classification

Song Huang*, Tiejun TONG, Hongyu Zhao

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

20 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1096-1106
Number of pages11
Issue number4
Publication statusPublished - Dec 2010

Scopus Subject Areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

User-Defined Keywords

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


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