General linear hypothesis testing of high-dimensional mean vectors with unequal covariance matrices based on random integration
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913556389167104 |
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| author | Cao, Mingxiang Qiu, Yelong Park, Junyong |
| author_facet | Cao, Mingxiang Qiu, Yelong Park, Junyong |
| contents | This paper is devoted to the study of the general linear hypothesis testing (GLHT) problem of multi-sample high-dimensional mean vectors. For the GLHT problem, we introduce a test statistic based on $L^2$-norm and random integration method, and deduce the asymptotic distribution of the statistic under given conditions. Finally, the potential advantages of our test statistics are verified by numerical simulation studies and examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14120 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | General linear hypothesis testing of high-dimensional mean vectors with unequal covariance matrices based on random integration Cao, Mingxiang Qiu, Yelong Park, Junyong Statistics Theory Primary 62H15, secondary 62E20 This paper is devoted to the study of the general linear hypothesis testing (GLHT) problem of multi-sample high-dimensional mean vectors. For the GLHT problem, we introduce a test statistic based on $L^2$-norm and random integration method, and deduce the asymptotic distribution of the statistic under given conditions. Finally, the potential advantages of our test statistics are verified by numerical simulation studies and examples. |
| title | General linear hypothesis testing of high-dimensional mean vectors with unequal covariance matrices based on random integration |
| topic | Statistics Theory Primary 62H15, secondary 62E20 |
| url | https://arxiv.org/abs/2410.14120 |