Universally Consistent K-Sample Tests via Dependence Measures
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2019
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| _version_ | 1866916420830363648 |
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| author | Panda, Sambit Shen, Cencheng Perry, Ronan Zorn, Jelle Lutz, Antoine Priebe, Carey E. Vogelstein, Joshua T. |
| author_facet | Panda, Sambit Shen, Cencheng Perry, Ronan Zorn, Jelle Lutz, Antoine Priebe, Carey E. Vogelstein, Joshua T. |
| contents | The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution. Analysis of variance is arguably the most classical method to test mean differences, along with several recent methods to test distributional differences. In this paper, we demonstrate the existence of a transformation that allows K-sample testing to be carried out using any dependence measure. Consequently, universally consistent K-sample testing can be achieved using a universally consistent dependence measure, such as distance correlation and the Hilbert-Schmidt independence criterion. This enables a wide range of dependence measures to be easily applied to K-sample testing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1910_08883 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Universally Consistent K-Sample Tests via Dependence Measures Panda, Sambit Shen, Cencheng Perry, Ronan Zorn, Jelle Lutz, Antoine Priebe, Carey E. Vogelstein, Joshua T. Machine Learning The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution. Analysis of variance is arguably the most classical method to test mean differences, along with several recent methods to test distributional differences. In this paper, we demonstrate the existence of a transformation that allows K-sample testing to be carried out using any dependence measure. Consequently, universally consistent K-sample testing can be achieved using a universally consistent dependence measure, such as distance correlation and the Hilbert-Schmidt independence criterion. This enables a wide range of dependence measures to be easily applied to K-sample testing. |
| title | Universally Consistent K-Sample Tests via Dependence Measures |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1910.08883 |