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020 _a9783032100047
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024 7 _a10.1007/978-3-032-10004-7
_2doi
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082 0 4 _a001.433
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245 1 0 _aFoundations and Advances of Machine Learning in Official Statistics
_h[electronic resource] /
_cedited by Florian Dumpert.
250 _a1st ed. 2025.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2025.
300 _aXIX, 373 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
341 0 _bPDF/UA-1
_2onix
341 0 _bTable of contents navigation
_2onix
341 0 _bSingle logical reading order
_2onix
341 0 _bShort alternative textual descriptions
_2onix
341 0 _bUse of color is not sole means of conveying information
_2onix
341 0 _bUse of high contrast between text and background color
_2onix
341 0 _bNext / Previous structural navigation
_2onix
341 0 _bAll non-decorative content supports reading without sight
_2onix
347 _atext file
_bPDF
_2rda
490 1 _aSociety, Environment and Statistics,
_x2948-2771
505 0 _aIntroduction -- 1. ML in official statistics (T Augustin, AL Boulesteix - LMU Munich) -- 2. Evaluation of generalization error (B Bischl, AL Boulesteix, R Hornung, H Kümpel, S Fischer, A Bender, L Bothman, L Schneider -- LMU Munich) -- 3. ML and Design of Experiments/Sample size calculation (T Augustin - LMU Munich) -- 4. Interpretable ML (B Bischl, L Bothmann, S Dandl, G Casalicchio -- LMU Munich) -- 5. Set-valued methods for ML in official statistics (T Augustin - LMU Munich) -- 6. Ethics and Fairness (F Kreuter - at LMU Munich) -- 7. Quality aspects of ML (Y Saidani et al -- Statistical Offices in Germany) -- 8. A statistical matching pipeline (T Küntzler --- Destatis) -- 9. Legal Aspects of ML (T Fetzer - Mannheim University).
506 0 _aOpen Access
520 _aThis 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.
532 8 _aAccessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub.
532 8 _aNo reading system accessibility options actively disabled
532 8 _aPublisher contact for further accessibility information: accessibilitysupport@springernature.com
650 0 _aSampling (Statistics).
_91108
650 0 _aMachine learning.
650 0 _aQuantitative research.
_9300
650 1 4 _aMethodology of Data Collection and Processing.
_91112
650 2 4 _aMachine Learning.
650 2 4 _aData Analysis and Big Data.
_9305
650 2 4 _aStatistical Learning.
700 1 _aDumpert, Florian.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_923416
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783032100030
776 0 8 _iPrinted edition:
_z9783032100054
776 0 8 _iPrinted edition:
_z9783032100061
830 0 _aSociety, Environment and Statistics,
_x2948-2771
_923417
856 4 0 _uhttps://doi.org/10.1007/978-3-032-10004-7
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
912 _aZDB-2-SOB
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