A new multivariate and non-parametric association measure based on paired orthants
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916122470645760 |
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| author | Martinez-Rabert, Eloi |
| author_facet | Martinez-Rabert, Eloi |
| contents | Multivariate correlation analysis plays a key role in various fields such as statistics and big data analytics. In this paper, it is presented a new non-parametric association measure between more than two variables based on the concept of paired orthants. In order to evaluate the proposed methodology, different N-tuple sets (from two to six variables) have been evaluated. The presented rank correlation analysis not only evaluates the inter-relatedness of multiple variables, but also determine the specific tendency of these variables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_01062 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A new multivariate and non-parametric association measure based on paired orthants Martinez-Rabert, Eloi Methodology 62H20 (Primary) Multivariate correlation analysis plays a key role in various fields such as statistics and big data analytics. In this paper, it is presented a new non-parametric association measure between more than two variables based on the concept of paired orthants. In order to evaluate the proposed methodology, different N-tuple sets (from two to six variables) have been evaluated. The presented rank correlation analysis not only evaluates the inter-relatedness of multiple variables, but also determine the specific tendency of these variables. |
| title | A new multivariate and non-parametric association measure based on paired orthants |
| topic | Methodology 62H20 (Primary) |
| url | https://arxiv.org/abs/2308.01062 |