Foundations and Advances of Machine Learning in Official Statistics / edited by Florian Dumpert.

Format:

Book

Contributor:

Dumpert, Florian., Editor.

Series:

Society, Environment and Statistics, 2948-2771

Language:

English

Subjects (All):

Sampling (Statistics).

Machine learning.

Quantitative research.

Methodology of Data Collection and Processing.

Machine Learning.

Data Analysis and Big Data.

Statistical Learning.

Local Subjects:

Methodology of Data Collection and Processing.

Machine Learning.

Data Analysis and Big Data.

Statistical Learning.

Physical Description:

1 online resource (XIX, 373 p. 1 illus.)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

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.

Contents:

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

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).

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-032-10004-6

9783032100047

OCLC:

1565878814