Overview
- This book is open access, which means that you have free and unlimited access
- Highlights the use of machine learning in official statistics
- Addresses methodological challenges relating to machine learning at the interface between application and basic research
- Embeds machine learning in the system of official statistics
Part of the book series: Society, Environment and Statistics (SESTAT)
Buy print copy
Tax calculation will be finalised at checkout
About this book
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
Similar content being viewed by others
Table of contents (14 chapters)
-
Front Matter
-
Methodological Aspects
-
Front Matter
-
-
Legal, Ethical, and Quality Aspects
-
Front Matter
-
-
Technological Aspects
-
Front Matter
-
-
Use Cases and Insights
-
Front Matter
-
Editors and Affiliations
About the editor
Florian Dumpert heads a division at the Federal Statistical Office of Germany that develops methodological and technological solutions and architectures for statistics production. The focus of his work is on the quality-assured integration and use of machine learning for the purpose of digitalisation, standardisation and automation of official statistics. His research interests include statistical machine learning, statistical data processing and imputation. He regularly participates in national and international projects on these topics and represents the disciplines in relevant working groups and committees.
Accessibility Information
PDF accessibility 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.
EPUB accessibility summary
This ebook is designed with accessibility in mind, aiming to meet the ePub Accessibility 1.0 AA and WCAG 2.2 Level AA standards. It features a navigable table of contents, structured headings, and alternative text for images, ensuring smooth, intuitive navigation and comprehension. The text is reflowable and resizable, with sufficient contrast. 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.
Bibliographic Information
Book Title: Foundations and Advances of Machine Learning in Official Statistics
Editors: Florian Dumpert
Series Title: Society, Environment and Statistics
DOI: https://doi.org/10.1007/978-3-032-10004-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
Hardcover ISBN: 978-3-032-10003-0Published: 12 December 2025
Softcover ISBN: 978-3-032-10006-1Due: 26 December 2026
eBook ISBN: 978-3-032-10004-7Published: 11 December 2025
Series ISSN: 2948-2763
Series E-ISSN: 2948-2771
Edition Number: 1
Number of Pages: XIX, 373
Number of Illustrations: 1 b/w illustrations
Topics: Statistics, general, Machine Learning, Statistics and Computing/Statistics Programs