Independence Testing for Mixed Data
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911079606517760 |
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| author | Jelić, Dana Bucalo Cuparić, Marija Milošević, Bojana |
| author_facet | Jelić, Dana Bucalo Cuparić, Marija Milošević, Bojana |
| contents | We consider the problem of testing independence in mixed-type data that combine count variables with positive, absolutely continuous variables. We first introduce two distinct classes of test statistics in the bivariate setting, designed to test independence between the components of a bivariate mixed-type vector. These statistics are then extended to the multivariate context to accommodate: (i) testing independence between vectors of different types and possibly different dimensions, and (ii) testing total independence among all components of vectors with different types. The construction is based on the recently introduced Baringhaus-Gaigall transformation, which characterizes the joint distribution of such data. We establish the asymptotic properties of the resulting tests and, through an extensive power study, demonstrate that the proposed approach is both competitive and flexible. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20609 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Independence Testing for Mixed Data Jelić, Dana Bucalo Cuparić, Marija Milošević, Bojana Methodology Statistics Theory Computation 62G10, 62H15, 62E20 We consider the problem of testing independence in mixed-type data that combine count variables with positive, absolutely continuous variables. We first introduce two distinct classes of test statistics in the bivariate setting, designed to test independence between the components of a bivariate mixed-type vector. These statistics are then extended to the multivariate context to accommodate: (i) testing independence between vectors of different types and possibly different dimensions, and (ii) testing total independence among all components of vectors with different types. The construction is based on the recently introduced Baringhaus-Gaigall transformation, which characterizes the joint distribution of such data. We establish the asymptotic properties of the resulting tests and, through an extensive power study, demonstrate that the proposed approach is both competitive and flexible. |
| title | Independence Testing for Mixed Data |
| topic | Methodology Statistics Theory Computation 62G10, 62H15, 62E20 |
| url | https://arxiv.org/abs/2507.20609 |