Shrinkage-based diagonal Hotelling's tests for high-dimensional small sample size data

Kai Dong, Herbert Pang, Tiejun TONG*, Marc G. Genton

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

High-throughput expression profiling techniques bring novel tools and also statistical challenges to genetic research. In addition to detecting differentially expressed genes, testing the significance of gene sets or pathway analysis has been recognized as an equally important problem. Owing to the "large p small n" paradigm, the traditional Hotelling's T2 test suffers from the singularity problem and therefore is not valid in this setting. In this paper, we propose a shrinkage-based diagonal Hotelling's test for both one-sample and two-sample cases. We also suggest several different ways to derive the approximate null distribution under different scenarios of p and n for our proposed shrinkage-based test. Simulation studies show that the proposed method performs comparably to existing competitors when n is moderate or large, but it is better when n is small. In addition, we analyze four gene expression data sets and they demonstrate the advantage of our proposed shrinkage-based diagonal Hotelling's test.

Original languageEnglish
Pages (from-to)127-142
Number of pages16
JournalJournal of Multivariate Analysis
Volume143
DOIs
Publication statusPublished - 1 Jan 2016

Scopus Subject Areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Diagonal Hotelling's test
  • High-dimensional data
  • Microarray data
  • Null distribution
  • Optimal variance estimation

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