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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2507.19413 |
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| _version_ | 1866917193477783552 |
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| author | Williams, Nicholas T. Hines, Oliver J. Rudolph, Kara E. |
| author_facet | Williams, Nicholas T. Hines, Oliver J. Rudolph, Kara E. |
| contents | The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. This literature is increasingly invoking the Riesz representation theorem and Riesz regression. This paper aims to introduce the Riesz representation theorem to an epidemiologic audience, explaining what it is and why it's useful, using step-by-step worked examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19413 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Riesz representers for the rest of us Williams, Nicholas T. Hines, Oliver J. Rudolph, Kara E. Statistics Theory The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. This literature is increasingly invoking the Riesz representation theorem and Riesz regression. This paper aims to introduce the Riesz representation theorem to an epidemiologic audience, explaining what it is and why it's useful, using step-by-step worked examples. |
| title | Riesz representers for the rest of us |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2507.19413 |