TY  - BOOK
AU  - Dumpert,Florian
ED  - SpringerLink (Online service)
TI  - Foundations and Advances of Machine Learning in Official Statistics
T2  - Society, Environment and Statistics,
SN  - 9783032100047
AV  - QA276.6 
U1  - 001.433 23
PY  - 2025///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Sampling (Statistics)
KW  - Machine learning
KW  - Quantitative research
KW  - Methodology of Data Collection and Processing
KW  - Machine Learning
KW  - Data Analysis and Big Data
KW  - Statistical Learning
N1  - Introduction -- 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); Open Access
N2  - 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
UR  - https://doi.org/10.1007/978-3-032-10004-7
ER  -