Foundations and Advances of Machine Learning in Official Statistics
This Open access book gives an overview of current research and developments on the incorporation of machine learning in official statistics. It covers methodological questions, practical aspects and cross-cutting issues. Machine learning has become an integral part of official statistics over the l...
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| Materialtyp: | Online |
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| Språk: | engelska |
| Utgiven: |
Springer Nature
2025
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| Ämnen: | |
| Länkar: | ONIX_20251218T100927_9783032100047_65 |
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| _version_ | 1869520785969774592 |
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| collection | Directory of Open Access Books |
| description | This Open access book gives an overview of current research and developments on the incorporation of machine learning in official statistics. It covers methodological questions, practical aspects and cross-cutting issues. Machine learning has become an integral part of official statistics over the last decade. This is evident in its many applications in numerous countries and organisations. At the same time, the integration of machine learning into statistical production raises questions about the right mathematical and statistical methodology, the consideration of quality standards and the appropriate IT support. In its four sections, "Methodological aspects", "Legal, ethical, and quality aspects", "Technological aspects" and "Use cases and insights", the book highlights current developments, provides inspiration, outlines challenges and offers possible solutions. It is aimed at methodologists in statistical offices and comparable institutions as well as scientists who are concerned with the further development and responsible use of machine learning |
| format | Online |
| id | doab-20.500.12854ir-170394 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1703942025-12-20T05:20:20Z Foundations and Advances of Machine Learning in Official Statistics Dumpert, Florian Open Access Machine Learning Deep Learning Artificial Intelligence Official Statistics National Statistical Institutes Methodology Quality MLOps Classification & Coding Editing & Imputation Streamlining of Processes thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UN Databases This Open access book gives an overview of current research and developments on the incorporation of machine learning in official statistics. It covers methodological questions, practical aspects and cross-cutting issues. Machine learning has become an integral part of official statistics over the last decade. This is evident in its many applications in numerous countries and organisations. At the same time, the integration of machine learning into statistical production raises questions about the right mathematical and statistical methodology, the consideration of quality standards and the appropriate IT support. In its four sections, "Methodological aspects", "Legal, ethical, and quality aspects", "Technological aspects" and "Use cases and insights", the book highlights current developments, provides inspiration, outlines challenges and offers possible solutions. It is aimed at methodologists in statistical offices and comparable institutions as well as scientists who are concerned with the further development and responsible use of machine learning 2025-12-20T05:20:19Z 2025-12-20T05:20:19Z 2025-12-18T09:18:18Z 2025 book ONIX_20251218T100927_9783032100047_65 https://library.oapen.org/handle/20.500.12657/109358 9783032100047 9783032100030 https://directory.doabooks.org/handle/20.500.12854/170394 eng Society, Environment and Statistics; Mathematics and Statistics; Mathematics and Statistics (R0) open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/109358/1/9783032100047.pdf Springer Nature Springer 10.1007/978-3-032-10004-7 10.1007/978-3-032-10004-7 9fa3421d-f917-4153-b9ab-fc337c396b5a 74b2c45d-3f4e-4abb-92b3-ea75c978380e 9783032100047 9783032100030 Springer 373 Cham [...] open access |
| spellingShingle | Open Access Machine Learning Deep Learning Artificial Intelligence Official Statistics National Statistical Institutes Methodology Quality MLOps Classification & Coding Editing & Imputation Streamlining of Processes thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UN Databases Foundations and Advances of Machine Learning in Official Statistics |
| title | Foundations and Advances of Machine Learning in Official Statistics |
| title_full | Foundations and Advances of Machine Learning in Official Statistics |
| title_fullStr | Foundations and Advances of Machine Learning in Official Statistics |
| title_full_unstemmed | Foundations and Advances of Machine Learning in Official Statistics |
| title_short | Foundations and Advances of Machine Learning in Official Statistics |
| title_sort | foundations and advances of machine learning in official statistics |
| topic | Open Access Machine Learning Deep Learning Artificial Intelligence Official Statistics National Statistical Institutes Methodology Quality MLOps Classification & Coding Editing & Imputation Streamlining of Processes thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UN Databases |
| topic_facet | Open Access Machine Learning Deep Learning Artificial Intelligence Official Statistics National Statistical Institutes Methodology Quality MLOps Classification & Coding Editing & Imputation Streamlining of Processes thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UN Databases |
| url | ONIX_20251218T100927_9783032100047_65 |