Two-sample Behrens-Fisher problem for high-dimensional data

Long Feng, Changliang Zou, Zhaojun Wang, Lixing ZHU

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

This article is concerned with the two-sample Behrens-Fisher problem in high-dimensional settings. A test is proposed that is scale-invariant, asymptotically normal under certain mild conditions, and the dimensionality is allowed to grow in the rate, respectively, from square to cube of the sample size in different scenarios. We explain the necessity of bias correction for existing scale-invariant tests. We also give some examples to show the advantage of the scale-invariant test over scale-variant tests when variances of the two samples are different.

Original languageEnglish
Pages (from-to)1297-1312
Number of pages16
JournalStatistica Sinica
Volume25
Issue number4
DOIs
Publication statusPublished - Oct 2015

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Asymptotic normality
  • Behrens-Fisher problem
  • High-dimensional data
  • Large-p-small-n
  • Two-sample test

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