High-dimensional testing for proportional covariance matrices

Koji Tsukuda, Shun Matsuura

研究成果: Article査読

3 被引用数 (Scopus)

抄録

Hypothesis testing for the proportionality of covariance matrices is a classical statistical problem and has been widely studied in the literature. However, there have been few treatments of this test in high-dimensional settings, especially for the case where the number of variables is larger than the sample size, despite high-dimensional statistical inference having recently received considerable attention. This paper studies hypothesis testing for the proportionality of two covariance matrices in the high-dimensional setting: m,n≍p δ for some δ∈(1∕2,1), where m and n denote the sample sizes and p denotes the number of variables. A test statistic is proposed and its asymptotic distribution is derived under multivariate normality. The non-asymptotic performance of the proposed test procedure is numerically examined.

本文言語English
ページ(範囲)412-420
ページ数9
ジャーナルJournal of Multivariate Analysis
171
DOI
出版ステータスPublished - 2019 5

ASJC Scopus subject areas

  • 統計学および確率
  • 数値解析
  • 統計学、確率および不確実性

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