Practical Kernel Tests of Conditional Independence
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866911163848065024 |
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| author | Pogodin, Roman Schrab, Antonin Li, Yazhe Sutherland, Danica J. Gretton, Arthur |
| author_facet | Pogodin, Roman Schrab, Antonin Li, Yazhe Sutherland, Danica J. Gretton, Arthur |
| contents | We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing is to obtain the correct test level (the specified upper bound on the rate of false positives), while still attaining competitive test power. Excess false positives arise due to bias in the test statistic, which is in our case obtained using nonparametric kernel ridge regression. We propose SplitKCI, an automated method for bias control for the Kernel-based Conditional Independence (KCI) test based on data splitting. We show that our approach significantly improves test level control for KCI without sacrificing test power, both theoretically and for synthetic and real-world data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Practical Kernel Tests of Conditional Independence Pogodin, Roman Schrab, Antonin Li, Yazhe Sutherland, Danica J. Gretton, Arthur Machine Learning We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing is to obtain the correct test level (the specified upper bound on the rate of false positives), while still attaining competitive test power. Excess false positives arise due to bias in the test statistic, which is in our case obtained using nonparametric kernel ridge regression. We propose SplitKCI, an automated method for bias control for the Kernel-based Conditional Independence (KCI) test based on data splitting. We show that our approach significantly improves test level control for KCI without sacrificing test power, both theoretically and for synthetic and real-world data. |
| title | Practical Kernel Tests of Conditional Independence |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.13196 |