High dimensional Mean Test for Temporal Dependent Data
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917110938075136 |
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| author | Hu, Yuchen Wang, Xiaoyi Feng, Long |
| author_facet | Hu, Yuchen Wang, Xiaoyi Feng, Long |
| contents | This paper proposes a novel test method for high-dimensional mean testing regard for the temporal dependent data. Comparison to existing methods, we establish the asymptotic normality of the test statistic without relying on restrictive assumptions, such as Gaussian distribution or M-dependence. Importantly, our theoretical framework holds potential for extension to other high-dimensional problems involving temporal dependent data. Additionally, our method offers significantly reduced computational complexity, making it more practical for large-scale applications. Simulation studies further demonstrate the computational advantages and performance improvements of our test. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22762 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High dimensional Mean Test for Temporal Dependent Data Hu, Yuchen Wang, Xiaoyi Feng, Long Methodology This paper proposes a novel test method for high-dimensional mean testing regard for the temporal dependent data. Comparison to existing methods, we establish the asymptotic normality of the test statistic without relying on restrictive assumptions, such as Gaussian distribution or M-dependence. Importantly, our theoretical framework holds potential for extension to other high-dimensional problems involving temporal dependent data. Additionally, our method offers significantly reduced computational complexity, making it more practical for large-scale applications. Simulation studies further demonstrate the computational advantages and performance improvements of our test. |
| title | High dimensional Mean Test for Temporal Dependent Data |
| topic | Methodology |
| url | https://arxiv.org/abs/2511.22762 |