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@BOOK{Dumpert:645904,
      key          = {645904},
      editor       = {Dumpert, Florian},
      title        = {{F}oundations and advances of machine learning in official
                      statistics},
      address      = {Cham},
      publisher    = {Springer},
      reportid     = {PUBDB-2026-00676},
      isbn         = {9783032100047},
      series       = {Society, environment and statistics},
      pages        = {1 Online-Ressource (XIX, 373 Seiten)},
      year         = {2025},
      abstract     = {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},
      ddc          = {001.433},
      typ          = {PUB:(DE-HGF)3},
      doi          = {10.1007/978-3-032-10004-7},
      url          = {https://bib-pubdb1.desy.de/record/645904},
}