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A generalized likelihood ratio test for linear hypothesis of k-sample means in high dimension

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  • Mingxiang Cao
  • Shiting Liang

Abstract

In this paper, we propose a new test for linear hypothesis of k-sample mean vectors in high-dimensional normal models based on generalized likelihood ratio method. The proposed test is designed for the “large p small n” situation where the data dimension p is much larger than the sample size n. The asymptotic null and non null distributions of the proposed test are derived under mild conditions. Simulation results show that our new test outperforms some competitors in both size and power. Moreover, our new test can also be applied to non normal data.

Suggested Citation

  • Mingxiang Cao & Shiting Liang, 2023. "A generalized likelihood ratio test for linear hypothesis of k-sample means in high dimension," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(24), pages 8725-8737, December.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:24:p:8725-8737
    DOI: 10.1080/03610926.2022.2069820
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