Due to the Covid-19 pandemic, the Statistical Data Editing Workshop 2020 was held as an online workshop.
| Document Title | Documents | Presentations |
|---|---|---|
| ENG | ENG | |
| Information Notice 1 | - | |
| Programme | - | |
| Meeting report | ||
| Opening | ||
| Opening of the workshop - Taeke Gjaltema (UNECE) |
- | Presentation |
| Opening of the workshop - Daniel Kilchmann (Federal Statistics Office, Switzerland) |
- | Presentation |
| Keynote presentation | ||
| Recent advances in imputation methods - Yves Tille (University of Neuchatel) |
- | Presentation |
| Statistical data cleaning for official statistics with R - Mark van der Loo (Statistics Netherlands) |
- | Presentation |
| Topic - Quality: assessing data quality and indicators | ||
| Introduction to the topic - Sander Scholtus (Statistics Netherlands) and Pedro Revilla (INE, Spain) |
- | Presentation |
| Evaluating Imputation Methods using ImpACT: First Case Study - Darren Gray (Statistics Canada) |
Paper | Presentation |
| Variance estimation after mass imputation with an application to the Dutch population census - Sander Scholtus (Statistics Netherlands) |
Paper | Presentation |
| General discussion | - | Presentation |
| Topic - Imputation Methods: machine learning and and new/emerging methods | ||
| Introduction to the topic - Sander Scholtus (Statistics Netherlands) and Li-Chun Zhang (Statistics Norway) |
- | Presentation |
| Wage Imputation with Deep Learning in the French Labor Force Survey - Damien Babet (Insee, France) |
Paper | Presentation |
| Bayesian Estimation of Linear Dynamic Panel Models with Missing Values - Marcel Preising (Federal Statistical Office, Germany) |
Paper | Presentation |
| Outlier detection and imputation using ML - Susie Jentoft (Statistics Norway) |
Paper | Presentation |
| RBEIS: A robust nearest neighbour donor imputation system implemented in SAS - Fern Leather (Office for National Statistics, UK) |
Abstract | Presentation |
| General discussion | - | Presentation |
| Topic - Methods: for machine learning and time series data, and new/emerging methods | ||
| Introduction to the topic - Darren Gray (Statistics Canada) and Daniel Kilchmann (Federal Statistics Office, Switzerland) |
- | Presentation |
| The UNECE High-Level-Group for the Modernization of Official Statistics Machine Learning Project: A report of the Editing & Imputation Group - Florian Dumpert (Federal Statistical Office, Germany) |
Paper | Presentation |
| Editing of Social Survey Data - Claus Sthamer (Office for National Statistics, UK) |
Paper | Presentation |
| ML to identify patterns behind errors in STS statistics - Fabiana Rocci (Istat, Italy) |
Paper | Presentation |
| Two-Phase Learning - Tatsiana Pekarskaya (Statistics Norway) |
Paper | Presentation |
| General discussion | - | Presentation |
| Topic - Processes: editing in a generic process, standardisation and meta-data driven processes | ||
| Introduction to the topic - Agnes Andics (Central Statistical Office, Hungary) and Simona Rosati (Istat, Italy) |
- | Presentation |
| Generic Statistical Data Editing Model (GSDEM) - Daniel Kilchmann (Federal Statistics Office, Switzerland) |
- | Presentation |
| Implementing main types of International validation rules in national validation processes - Olav ten Bosch (Statistics Netherlands) |
Paper | Presentation |
| Modern, process oriented and metadata driven statistical production - Anna Długosz (Statistics Poland) |
Paper | Presentation |
| Automation of E & I Processes - Kerstin Lange (Federal Statistical Office, Germany) |
Paper | Presentation |
| General discussion | - | Presentation |
| Topic - Data: 2021 Census, administrative data, geospatial data, big data and other alternative data | ||
| Introduction to the topic - Fern Leather (Office of National Statistics, UK) and Li-Chun Zhang (Statistics Norway) |
- | Presentation |
| Webscraped data for replacing and validating survey questions - Johannes Gussenbauer (Statistics Austria) |
Paper | Presentation |
| An imputation procedure for the Italian attained level of education in the register of individuals based on administrative and survey data - Romina Filippini (Istat, Italy) |
Paper | Presentation |
| Use of administrative data and alternative data for census when applying modern technologies - Janusz Dygaszewicz (Statistics Poland) |
Paper | Presentation |
| An overview of the editing and imputation process of the 2018 Italian Permanent census - Francesco Scalfati (Istat, Italy) |
Paper | Presentation |
| General discussion | - | Presentation |
| Poster session | ||
| Robust Tools for Statistical Data Editing and Imputation - Kazumi Wada (Tsuda University, Japan) |
Paper Poster |
Presentation |
| Internal Information System. A possibility of low-cost data governance inside the National Statistical Offices - Tania Garcia (INEGI, Mexico) |
Paper Poster |
- |
| The imputation of the “Attained Level of Education” in the base register of individuals: an experimentation using Machine Learning techniques - Fabrizio DeFausti (Istat, Italy) |
Poster | Presentation |
| Profile of Manufacturing Exports Enterprise - Carlo Lopez (INEGI, Mexico) |
Paper Poster |
Presentation |
| Territorial preparation in census 2021 - Ludmila Ivancikova (Slovakia) |
Poster | - |
| Challenges and advancements in assessing data quality during the generation of criminal and justice statistics in Mexico - Ines Arce (INEGI, Mexico) |
Poster | - |
| Future work discussion | ||
| Short presentation about future work ideas received - Daniel Kilchmann (Federal Statistics Office, Switzerland) |
- | Presentation |
| Future work discussion - Daniel Kilchmann (Federal Statistics Office, Switzerland) |
- | Presentation |
