Testing independence in the presence of missing data: high-dimensional case
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866918466689171456 |
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| author | Cuparić, Marija Milošević, Bojana Radojević, Jelena |
| author_facet | Cuparić, Marija Milošević, Bojana Radojević, Jelena |
| contents | In this paper, we consider the problem of testing independence in high-dimensional settings with missing data. Building upon a recently proposed Kendall-based statistic, we introduce two new modifications specifically designed to accommodate incomplete observations. The proposed methods are studied from both theoretical and empirical perspectives. A comprehensive simulation study illustrates the robustness and applicability of the new approaches. The findings contribute to the development of nonparametric methods for analyzing high-dimensional and incomplete data structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22980 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Testing independence in the presence of missing data: high-dimensional case Cuparić, Marija Milošević, Bojana Radojević, Jelena Methodology Statistics Theory In this paper, we consider the problem of testing independence in high-dimensional settings with missing data. Building upon a recently proposed Kendall-based statistic, we introduce two new modifications specifically designed to accommodate incomplete observations. The proposed methods are studied from both theoretical and empirical perspectives. A comprehensive simulation study illustrates the robustness and applicability of the new approaches. The findings contribute to the development of nonparametric methods for analyzing high-dimensional and incomplete data structures. |
| title | Testing independence in the presence of missing data: high-dimensional case |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2604.22980 |