Interpoint distance based two sample tests in high dimension

Changbo Zhu, Xiaofeng Shao

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we study a class of two sample test statistics based on inter-point distances in the high dimensional and low/medium sample size setting. Our test statistics include the well-known energy distance and maximum mean discrepancy with Gaussian and Laplacian kernels, and the critical values are obtained via permutations. We show that all these tests are inconsistent when the two high dimensional distributions correspond to the same marginal distributions but differ in other aspects of the distributions. The tests based on energy distance and maximum mean discrepancy mainly target the differences between marginal means and variances, whereas the test based on L1-distance can capture the difference in marginal distributions. Our theory sheds new light on the limitation of inter-point distance based tests, the impact of different distance metrics, and the behavior of permutation tests in high dimension. Some simulation results and a real data illustration are also presented to corroborate our theoretical findings.

Original languageEnglish (US)
Pages (from-to)1189-1211
Number of pages23
JournalBernoulli
Volume27
Issue number2
DOIs
StatePublished - May 2021

Keywords

  • High dimensionality
  • Permutation test
  • Power analysis
  • Two sample test

ASJC Scopus subject areas

  • Statistics and Probability

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