Asymptotic linear dependence and ellipse statistics for multivariate two-sample homogeneity test
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908672095944704 |
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| author | Shen, Chifeng Fu, Yuejiao Chen, Michael Shi, Xiaoping |
| author_facet | Shen, Chifeng Fu, Yuejiao Chen, Michael Shi, Xiaoping |
| contents | Statistical depth, which measures the center-outward rank of a given sample with respect to its underlying distribution, has become a popular and powerful tool in nonparametric inference. In this paper, we investigate the use of statistical depth in multivariate two-sample problems. We propose a new depth-based nonparametric two-sample test, which has the Chi-square(1) asymptotic distribution under the null hypothesis. Simulations and real-data applications highlight the efficacy and practical value of the proposed test. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_19381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Asymptotic linear dependence and ellipse statistics for multivariate two-sample homogeneity test Shen, Chifeng Fu, Yuejiao Chen, Michael Shi, Xiaoping Methodology 62G10 Statistical depth, which measures the center-outward rank of a given sample with respect to its underlying distribution, has become a popular and powerful tool in nonparametric inference. In this paper, we investigate the use of statistical depth in multivariate two-sample problems. We propose a new depth-based nonparametric two-sample test, which has the Chi-square(1) asymptotic distribution under the null hypothesis. Simulations and real-data applications highlight the efficacy and practical value of the proposed test. |
| title | Asymptotic linear dependence and ellipse statistics for multivariate two-sample homogeneity test |
| topic | Methodology 62G10 |
| url | https://arxiv.org/abs/2511.19381 |