Generalized Independence Test for Modern Data
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929498434306048 |
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| author | Liu, Mingshuo Zhou, Doudou Chen, Hao |
| author_facet | Liu, Mingshuo Zhou, Doudou Chen, Hao |
| contents | The test of independence is a crucial component of modern data analysis. However, traditional methods often struggle with the complex dependency structures found in high-dimensional data. To overcome this challenge, we introduce a novel test statistic that captures intricate relationships using similarity and dissimilarity information derived from the data. The statistic exhibits strong power across a broad range of alternatives for high-dimensional data, as demonstrated in extensive simulation studies. Under mild conditions, we show that the new test statistic converges to the $χ^2_4$ distribution under the permutation null distribution, ensuring straightforward type I error control. Furthermore, our research advances the moment method in proving the joint asymptotic normality of multiple double-indexed permutation statistics. We showcase the practical utility of this new test with an application to the Genotype-Tissue Expression dataset, where it effectively measures associations between human tissues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07745 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generalized Independence Test for Modern Data Liu, Mingshuo Zhou, Doudou Chen, Hao Methodology Statistics Theory The test of independence is a crucial component of modern data analysis. However, traditional methods often struggle with the complex dependency structures found in high-dimensional data. To overcome this challenge, we introduce a novel test statistic that captures intricate relationships using similarity and dissimilarity information derived from the data. The statistic exhibits strong power across a broad range of alternatives for high-dimensional data, as demonstrated in extensive simulation studies. Under mild conditions, we show that the new test statistic converges to the $χ^2_4$ distribution under the permutation null distribution, ensuring straightforward type I error control. Furthermore, our research advances the moment method in proving the joint asymptotic normality of multiple double-indexed permutation statistics. We showcase the practical utility of this new test with an application to the Genotype-Tissue Expression dataset, where it effectively measures associations between human tissues. |
| title | Generalized Independence Test for Modern Data |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2409.07745 |