Leveraging Machine Learning for Official Statistics: A Statistical Manifesto
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914940576595968 |
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| author | Puts, Marco Salgado, David Daas, Piet |
| author_facet | Puts, Marco Salgado, David Daas, Piet |
| contents | It is important for official statistics production to apply ML with statistical rigor, as it presents both opportunities and challenges. Although machine learning has enjoyed rapid technological advances in recent years, its application does not possess the methodological robustness necessary to produce high quality statistical results. In order to account for all sources of error in machine learning models, the Total Machine Learning Error (TMLE) is presented as a framework analogous to the Total Survey Error Model used in survey methodology. As a means of ensuring that ML models are both internally valid as well as externally valid, the TMLE model addresses issues such as representativeness and measurement errors. There are several case studies presented, illustrating the importance of applying more rigor to the application of machine learning in official statistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04365 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Machine Learning for Official Statistics: A Statistical Manifesto Puts, Marco Salgado, David Daas, Piet Machine Learning Methodology 62D05, 68T05 I.2.6; G.3 It is important for official statistics production to apply ML with statistical rigor, as it presents both opportunities and challenges. Although machine learning has enjoyed rapid technological advances in recent years, its application does not possess the methodological robustness necessary to produce high quality statistical results. In order to account for all sources of error in machine learning models, the Total Machine Learning Error (TMLE) is presented as a framework analogous to the Total Survey Error Model used in survey methodology. As a means of ensuring that ML models are both internally valid as well as externally valid, the TMLE model addresses issues such as representativeness and measurement errors. There are several case studies presented, illustrating the importance of applying more rigor to the application of machine learning in official statistics. |
| title | Leveraging Machine Learning for Official Statistics: A Statistical Manifesto |
| topic | Machine Learning Methodology 62D05, 68T05 I.2.6; G.3 |
| url | https://arxiv.org/abs/2409.04365 |