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

Mingxiang Cao and Shiting Liang

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 24, 8725-8737

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.

Date: 2023
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DOI: 10.1080/03610926.2022.2069820

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