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Foundations and advances of machine learning in official statistics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Foundations and advances of machine learning in official statistics/ edited by Florian Dumpert.
other author:	Dumpert, Florian.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xix, 373 p. :ill., digital ;24 cm.
[NT 15003449]:	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).
Contained By:	Springer Nature eBook
Subject:	Statistics - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-032-10004-7
ISBN:	9783032100047

Foundations and advances of machine learning in official statistics
Foundations and advances of machine learning in official statistics[electronic resource] /edited by Florian Dumpert. - Cham :Springer Nature Switzerland :2025. - xix, 373 p. :ill., digital ;24 cm. - Society, environment and statistics,2948-2771. - Society, environment and statistics..

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.

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.

ISBN: 9783032100047

Standard No.: 10.1007/978-3-032-10004-7doiSubjects--Topical Terms:

535534
Statistics
--Data processing.

LC Class. No.: HA32

Dewey Class. No.: 001.4220285631

Foundations and advances of machine learning in official statistics
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245 0 0 $a Foundations and advances of machine learning in official statistics $h [electronic resource] / $c edited by Florian Dumpert.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xix, 373 p. : $b ill., digital ; $c 24 cm.
490 1 $a Society, environment and statistics, $x 2948-2771
505 0 $a 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).
506 $a Open access.
520 $a 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.
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650 0 $a Machine learning. $3 533906
650 1 4 $a Methodology of Data Collection and Processing. $3 3598081
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650 2 4 $a Data Analysis and Big Data. $3 3538537
650 2 4 $a Statistical Learning. $3 3597795
700 1 $a Dumpert, Florian. $3 3804981
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950 $a Mathematics and Statistics (SpringerNature-11649)