A new test on high-dimensional mean vector without any assumption on population covariance matrix

Shota Katayama, Yutaka Kano

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this paper, a new test for the equality of the mean vectors between a two groups with the same number of the observations in high-dimensional data. The existing tests for this problem require a strong condition on the population covariance matrix. The proposed test in this paper does not require such conditions for it. This test will be obtained in a general model, that is, the data need not be normally distributed.

Original languageEnglish
Pages (from-to)5290-5304
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Volume43
Issue number24
DOIs
Publication statusPublished - 2014 Dec 17
Externally publishedYes

Fingerprint

Covariance matrix
High-dimensional
High-dimensional Data
Equality
Model

Keywords

  • Asymptotic distributions
  • High-dimensional data
  • Testing mean vector

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

A new test on high-dimensional mean vector without any assumption on population covariance matrix. / Katayama, Shota; Kano, Yutaka.

In: Communications in Statistics - Theory and Methods, Vol. 43, No. 24, 17.12.2014, p. 5290-5304.

Research output: Contribution to journalArticle

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