Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data

Herbert Pang, Tiejun Tong, Hongyu Zhao

Research output: Contribution to journalArticlepeer-review

Abstract

High-dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k-nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage-based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases.
Original languageEnglish
Pages (from-to)1021-1029
Number of pages9
JournalBiometrics
Volume65
Issue number4
Early online date23 Nov 2009
DOIs
Publication statusPublished - Dec 2009

User-Defined Keywords

  • Discriminant analysis
  • Large p small n
  • Microarray
  • Regularization
  • Shrinkage
  • Tumor classification

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