The focus of the meeting will be on cutting edge ideas, approaches, and tools in the area of statistical data editing. In addition to the traditional presentations, the agenda of the meeting anticipates interactive discussions related to particular topics within this field.
The target audience of the expert meeting includes senior and middle-level methodologists, statisticians and researchers, working on editing and imputation of statistical data derived from surveys, censuses, administrative and external sources.
Detailed information and examples of topics to be covered in the meeting, registration, contributions and other organizational aspects can be found in Information Notice #1.
Logistical information concerning the event and Vienna itself can be found in Information Notice #2.
Templates can be downloaded here.
| Document Title | Abstracts & Papers | Presentations |
|---|---|---|
| Keynote Presentation: Current work on automatic multisource editing at Statistics Netherlands. Sander Scholtus (Statistics Netherlands) | ||
| Leveraging AI for statistical editing: the case of the BIS AI Metadata Editor. Olivier Sirello (Bank for International Settlements) | ||
| Data Observability: a general, flexible and scalable framework for data quality. Oscar Quintana (Bci Bank) | ||
| National guidelines on data editing; the foundation for building a solution for the future. Aslaug Hurlen Foss (Statistics Norway) | ||
| Moving towards the standardized process of automatic statistical data editing using machine learning techniques. Ieva Burakauskaitė (State Data Agency, Statistics Lithuania) | ||
| The editing and imputation process of the 2021 household and nuclei types reconstruction in Italy. Rosa Maria Lipsi (Istat) | ||
| Using hidden Markov and macro integration models for combining data from different sources . Nino Mushkudiani (Statistics Netherlands) | ||
| Organisational Aspects of Implementing ML Based Data Editing in Statistical Production. Steffan Moritz (Destatis) | ||
| Keynote Presentation. Darren Gray (Statistics Canada) | - | |
| Full conditional distributions for handling restrictions in the context of automated statistical data editing. Christian Aßmann (Leibniz Institute for Educational Trajectories) | ||
| Application of the MissForest algorithm for imputing income variables in the Survey on Income and Living Conditions. Blandine Bianchi (Swiss Federal Statistical Office) | ||
| Assessment of Manual vs Automated Survey Editing and Imputation. Sean Rhodes (U.S. Department of Agriculture National Agricultural Statistics Service) | ||
| Enhancing Official Statistics through Artificial Intelligence: A Comparative Study of Imputation Techniques. Simona Cafieri (Istat) | ||
| Detecting Extreme Numerical Outliers in Trade Data: A Novel Method for Highly Asymmetric Distributions. Andrea Cerasa (European Commission, Joint Research Centre) | ||
| Selective editing methods for the production of new Services Producer Price Indices (SPPIs) from indirect data sources. Simona Rosati (Istat) | ||
| Outlier Identification and Adjustment for Time Series. Markus Fröhlich (Statistik Austria) | ||
| Random forest imputation of nutritional information for statistics on food consumption in Norway. Susie Jentoft (Statistics Norway) | ||
| Enhancing GDP Estimates Through Comprehensive Economic Surveys and Advanced Data Analysis. Thuraya Alkharoosi (Dubai Data and Statistics) | ||
| The European One-Stop-Shop for Artificial Intelligence and Machine Learning for Official Statistics (AIML4OS): WP9 Use Case focused on imputation. David Salgado (Statistics Spain) | ||
| The European One-Stop-Shop for Artificial Intelligence and Machine Learning for Official Statistics (AIML4OS): WP8 Use Case focused on data editing. Steffen Moritz (Destatis) |