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

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Pang, H; Tong, T; Zhao, H

Published Date

  • December 2009

Published In

Volume / Issue

  • 65 / 4

Start / End Page

  • 1021 - 1029

PubMed ID

  • 19302409

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

Digital Object Identifier (DOI)

  • 10.1111/j.1541-0420.2009.01200.x

Language

  • eng

Conference Location

  • United States