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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.12066 |
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| _version_ | 1866910610304794624 |
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| author | Cao, Mingxiang Zhang, Hongwei Xu, Kai He, Daojiang |
| author_facet | Cao, Mingxiang Zhang, Hongwei Xu, Kai He, Daojiang |
| contents | In this paper, for the problem of heteroskedastic general linear hypothesis testing (GLHT) in high-dimensional settings, we propose a random integration method based on the reference L2-norm to deal with such problems. The asymptotic properties of the test statistic can be obtained under the null hypothesis when the relationship between data dimensions and sample size is not specified. The results show that it is more advisable to approximate the null distribution of the test using the distribution of the chi-square type mixture, and it is shown through some numerical simulations and real data analysis that our proposed test is powerful. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12066 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Linear hypothesis testing in high-dimensional heteroscedastics via random integration Cao, Mingxiang Zhang, Hongwei Xu, Kai He, Daojiang Statistics Theory 62H15, 62E20 In this paper, for the problem of heteroskedastic general linear hypothesis testing (GLHT) in high-dimensional settings, we propose a random integration method based on the reference L2-norm to deal with such problems. The asymptotic properties of the test statistic can be obtained under the null hypothesis when the relationship between data dimensions and sample size is not specified. The results show that it is more advisable to approximate the null distribution of the test using the distribution of the chi-square type mixture, and it is shown through some numerical simulations and real data analysis that our proposed test is powerful. |
| title | Linear hypothesis testing in high-dimensional heteroscedastics via random integration |
| topic | Statistics Theory 62H15, 62E20 |
| url | https://arxiv.org/abs/2409.12066 |