Professor Jörg Drechsler
Functions at the IAB
Professional background
Jörg Drechsler studied piano at the Nuremberg University of Music from 1999 to 2004 and economics at the University of Erlangen-Nuremberg from 2001 to 2006.
He has been a research assistant at the IAB since 2006. He received his doctorate from Otto Friedrich University in Bamberg in 2009 and his habilitation in statistics from Ludwig Maximilian University in Munich in 2015. From 2016, he was an adjunct assistant professor, and since 2020, he has been an associate research professor in the Joint Program in Survey Methodology at the University of Maryland, USA. From 2018 to 2024, he also held an honorary professorship at the Faculty of Social Sciences at the University of Mannheim.
Since 2024 Jörg Drechsler is Head of the Research Department “Statistical Methods” at the IAB and also holds the Chair of Statistics with Focus on Data Collection and Data Science in Labor Market Research at the Department of Statistics at LMU Munich.
Activities
Projects
ongoing Projects
- Transcription of audio data
- Questionnaire parser
- Non-Probability Sample Inferenz (NOSI)
- IAB - Online Panel for Labour Market Research
finished Projects
- Synthetic data in statistics and computer science - a systematic evaluation and methodological improvements
- Überarbeitung des IAB-Publikationsratings
- Towards an End-to-End Approach to Formal Privacy for Sample Surveys
- Enhancing the Quality and Utility of Longitudinal Data for Education Research
- Imputation and record linkage strategies for educational data collected from surveys and administrative sources
- Imputation of right-censored wages in the BeH
- Synthetic datasets for the geocoded IEB
- Imputation der Arbeitszeitinformationen in der BeH
- Imputation der Bildungsvariable in der IEB
- Entwurf eines Publikationsratings für das IAB
- Imputation und Gewichtung zum Umgang mit fehlenden Werten in hierarchischen Längsschnitterhebungen
- Generating synthetic datasets for the BHP
- Blue ETS - BLUE-Enterprise and Trade Statistics
- Verbesserung der informationellen Infrastruktur für das E-Science Age (infinitE)
- Wirtschaftsstatistische Paneldaten und faktische Anonymisierung (FAWE)
- Prüfung der Möglichkeiten der tieferen Regional- und Berufsgliederung sowie der multiplen Datenimputation der Quartalsdaten der Erhebung des gesamtwirtschaftlichen Stellenangebots 2005/2006
- Fragebogensplit Offene-Stellen Erhebung
Publications
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Does the Synthesis Model Influence a Subsequent Prediction of the Same Type
Fössing, E. & Drechsler, J. (2025): Does the Synthesis Model Influence a Subsequent Prediction of the Same Type. In: UNECE (Hrsg.) (2025): Expert Meeting on Statistical Data Confidentiality. 15-17 October 2025, p. 1-10.
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Buyer Beware: Understanding the trade-off between utility and risk in CART based models using simulation data
Latner, J., Neunhoeffer, M. & Drechsler, J. (2025): Buyer Beware: Understanding the trade-off between utility and risk in CART based models using simulation data. In: UNECE (Hrsg.) (2025): Expert Meeting on Statistical Data Confidentiality. 15-17 October 2025, p. 1-12.
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Imputation strategies for rightcensored wages in longitudinal datasets
Drechsler, J. & Ludsteck, J. (2025): Imputation strategies for rightcensored wages in longitudinal datasets. In: Journal for labour market research, Vol. 59. DOI:10.1186/s12651-025-00410-4
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A Consensus Privacy Metrics Framework for Synthetic Data
Pilgram, L., Dankar, F., Drechsler, J., Elliot, M., Domingo-Ferrer, J., Francis, P., Kantarcioglu, M., Kong, L., Malin, B., Muralidhar, K., Myles, P., Prasser, F., Raisaro, J., Yan, C. & El Emam, K. (2025): A Consensus Privacy Metrics Framework for Synthetic Data. In: Patterns, Vol. 6, No. 10. DOI:10.1016/j.patter.2025.101320
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An overview of data protection strategies for individual-level geocoded data
Steffen, M., Körner, K. & Drechsler, J. (2025): An overview of data protection strategies for individual-level geocoded data. In: Statistics surveys, Vol. 19, p. 1-27. DOI:10.1214/25-SS151
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On the Formal Privacy Guarantees of Synthetic Data (Generated Without Formal Privacy Guarantees) (im Erscheinen)
Neunhoeffer, M., Seeman, J. & Drechsler, J. (2026): On the Formal Privacy Guarantees of Synthetic Data (Generated Without Formal Privacy Guarantees) (im Erscheinen). In: Harvard Data Science Review.
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Handbook of Sharing Confidential Data
Drechsler, J., Kifer, D., Reiter, J. & Slavković, A. (eds.) (2025): Handbook of Sharing Confidential Data. Differential Privacy, Secure Multiparty Computation, and Synthetic Data. (Chapman & Hall/CRC Handbooks of Modern Statistical Methods), Boca Raton: CRC Press, 342 p.
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Bridging Between Different BeH Industry Classifications via Imputation
Drechsler, J. & Ludsteck, J. (2024): Bridging Between Different BeH Industry Classifications via Imputation. (FDZ-Methodenreport 04/2024 (en)), Nürnberg, 17 p. DOI:10.5164/IAB.FDZM.2404.en.v1
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Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling
Oganian, A., Drechsler, J. & Iqbal, M. (2024): Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, p. 129-143. DOI:10.1007/978-3-031-69651-0_9
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An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity
Fössing, E. & Drechsler, J. (2024): An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, p. 178-193. DOI:10.1007/978-3-031-69651-0_12
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Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator
Latner, J., Neunhoeffer, M. & Drechsler, J. (2024): Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, p. 115-128. DOI:10.1007/978-3-031-69651-0_8
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The Complexities of Differential Privacy for Survey Data
Drechsler, J. & Bailie, J. (2024): The Complexities of Differential Privacy for Survey Data. (NBER working paper / National Bureau of Economic Research 32905), Cambridge, Mass, 18 p.
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On the Formal Privacy Guarantees of Synthetic Data
Neunhoeffer, M., Latner, J. & Drechsler, J. (2024): On the Formal Privacy Guarantees of Synthetic Data. In: National Bureau of Economic Research (2024): Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, Spring 2024, Washington, p. 1-16.
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Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys
Bailie, J. & Drechsler, J. (2024): Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys. In: National Bureau of Economic Research (2024): Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, Spring 2024, Washington, p. 1-33.
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30 Years of Synthetic Data
Drechsler, J. & Haensch, A. (2024): 30 Years of Synthetic Data. In: Statistical Science, Vol. 39, No. 2, p. 221-242. DOI:10.1214/24-STS927
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Re-identification potential of structured health data
Drechsler, J. & Pauly, H. (2024): Das Reidentifikationspotenzial von strukturierten Gesundheitsdaten. In: Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, Vol. 67, p. 164-170. DOI:10.1007/s00103-023-03820-2
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An overview of data protection strategies for individual-level geocoded data
Steffen, M., Körner, K. & Drechsler, J. (2023): An overview of data protection strategies for individual-level geocoded data. In: United Nations Economic Commission for Europe (Hrsg.) (2023): UNECE Expert meeting on Statistical Data Confidentiality 2023, p. 1-13.
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Imputation der rechtszensierten Tagesentgelte für die BeH
Drechsler, J., Ludsteck, J. & Moczall, A. (2023): Imputation der rechtszensierten Tagesentgelte für die BeH. (FDZ-Methodenreport 05/2023 (de)), Nürnberg, 25 p. DOI:10.5164/IAB.FDZM.2305.de.v1
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Differential Privacy for Government Agencies - Are We There Yet?
Drechsler, J. (2023): Differential Privacy for Government Agencies - Are We There Yet? In: Journal of the American Statistical Association, Vol. 118, No. 541, p. 761-773. DOI:10.1080/01621459.2022.2161385
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Challenges in Measuring Utility for Fully Synthetic Data
Drechsler, J. (2022): Challenges in Measuring Utility for Fully Synthetic Data. In: J. Domingo-Ferrer & M. Laurent (Hrsg.) (2022): Privacy in Statistical Databases 2022, p. 220-233. DOI:10.1007/978-3-031-13945-1_16
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Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling
Bun, M., Drechsler, J., Gaboardi, M., McMillan, A. & Sarathy, J. (2022): Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling. In: L. Elisa Celis (Ed.) (2022): 3rd annual Symposium on Foundations of Responsible Computing (FORC), p. 1-24. DOI:10.4230/LIPIcs.FORC.2022.1
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Nonparametric Differentially Private Confidence Intervals for the Median
Drechsler, J., Globus-Harris, I., McMillan, A., Sarathy, J. & Smith, A. (2022): Nonparametric Differentially Private Confidence Intervals for the Median. In: Journal of survey statistics and methodology, Vol. 10, No. 3, p. 804-829. DOI:10.1093/jssam/smac021
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Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy
Hu, J., Drechsler, J. & Kim, H. (2022): Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy. In: Journal of survey statistics and methodology, Vol. 10, No. 3, p. 688-719. DOI:10.1093/jssam/smac012
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Herausforderungen bei der Anonymisierung - von der Pseudonymisierung über synthetische Daten zum Konzept der Differential Privacy
Drechsler, J. (2022): Herausforderungen bei der Anonymisierung - von der Pseudonymisierung über synthetische Daten zum Konzept der Differential Privacy. In: J. Baas (Hrsg.) (2022): Gesundheit im Zeitalter der Plattformökonomie, p. 80-88.
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Accounting for longitudinal data structures when disseminating synthetic data to the public
Rashid, S., Drechsler, J. & Mitra, R. (2021): Accounting for longitudinal data structures when disseminating synthetic data to the public. In: United Nations Economic Comission for Europe (Hrsg.) (2021): Expert Meeting on Statistical Data Confidentiality, Genf, p. 1-12.
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Synthesizing Geocodes to Facilitate Access to Detailed Geographical Information in Large-Scale Administrative Data
Drechsler, J. & Hu, J. (2021): Synthesizing Geocodes to Facilitate Access to Detailed Geographical Information in Large-Scale Administrative Data. In: Journal of survey statistics and methodology, Vol. 9, No. 3, p. 523-548. DOI:10.1093/jssam/smaa035
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Synthetic microdata for establishment surveys under informative sampling
Kim, H., Drechsler, J. & Thompson, K. (2021): Synthetic microdata for establishment surveys under informative sampling. In: Journal of the Royal Statistical Society. Series A, Statistics in Society, Vol. 184, No. 1, p. 255-281. DOI:10.1111/rssa.12622
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The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond
Speidel, M., Drechsler, J. & Jolani, S. (2020): The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond. In: Journal of statistical software, Vol. 95, No. 9, p. 1-48. DOI:10.18637/jss.v095.i09
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TippingSens: An R Shiny Application to Facilitate Sensitivity Analysis for Causal Inference Under Confounding
Haensch, A., Drechsler, J. & Bernhard, S. (2020): TippingSens: An R Shiny Application to Facilitate Sensitivity Analysis for Causal Inference Under Confounding. (IAB-Discussion Paper 29/2020), Nürnberg, 39 p.
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Secure Matrix Computation: A Viable Alternative to Record Linkage?
Drechsler, J. & Klein, B. (2020): Secure Matrix Computation: A Viable Alternative to Record Linkage? In: J. Domingo-Ferrer & K. Muralidhar (Hrsg.) (2020): Privacy in Statistical Databases, Cham, p. 240-254. DOI:10.1007/978-3-030-57521-2_17
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Applying data synthesis for longitudinal business data across three countries
Alam, M., Dostie, B., Drechsler, J. & Vilhuber, L. (2020): Applying data synthesis for longitudinal business data across three countries. In: Statistics in transition, Vol. 21, No. 4, p. 212-236. DOI:10.21307/stattrans-2020-039
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Multiple Imputation
Drechsler, J. (2020): Multiple Imputation. In: P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug & R. A. Williams (Hrsg.) (2020), SAGE Research methods foundations: an encyclopedia, o. Sz. DOI:10.4135/9781526421036885886
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In memory of Professor Susanne Rässler
Drechsler, J., Kiesl, H., Meinfelder, F., Raghunathan, T., Rubin, D., Schenker, N. & Zell, E. (2019): In memory of Professor Susanne Rässler. In: Journal of official statistics, Vol. 35, No. 1, p. 285-286. DOI:10.2478/jos-2019-0013
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Statistical matching as a supplement to record linkage
Gessendorfer, J., Beste, J., Drechsler, J. & Sakshaug, J. (2018): Statistical matching as a supplement to record linkage. A valuable method to tackle nonconsent bias? In: Journal of official statistics, Vol. 34, No. 4, p. 909-933. DOI:10.2478/jos-2018-0045
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Some clarifications regarding fully synthetic data
Drechsler, J. (2018): Some clarifications regarding fully synthetic data. In: J. Domingo-Ferrer & F. Montes (Hrsg.) (2018): Privacy in statistical databases : UNESCO Chair in Data Privacy International Conference, PSD 2018 Valencia, Spain, September 26 - 28, 2018 Proceedings (Lecture Notes in Computer Science, 11126), p. 109-121. DOI:10.1007/978-3-319-99771-1_8
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R package hmi: a convenient tool for hierarchical multiple imputation and beyond
Speidel, M., Drechsler, J. & Jolani, S. (2018): R package hmi: a convenient tool for hierarchical multiple imputation and beyond. (IAB-Discussion Paper 16/2018), Nürnberg, 55 p.
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Preface to the papers on 'Data confidentiality and statistical disclosure control'
Drechsler, J. & Shlomo, N. (2019): Preface to the papers on 'Data confidentiality and statistical disclosure control'. In: Journal of the Royal Statistical Society. Series A, Statistics in Society, Vol. 181, No. 3, p. 607-608. DOI:10.1111/rssa.12383
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Synthetische Daten
Drechsler, J. & Jentzsch, N. (2018): Synthetische Daten. Innovationspotential und gesellschaftliche Herausforderungen. Berlin, 26 p.
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Biases in multilevel analyses caused by cluster-specific fixed-effects imputation
Speidel, M., Drechsler, J. & Sakshaug, J. (2018): Biases in multilevel analyses caused by cluster-specific fixed-effects imputation. In: Behavior research methods, Vol. 50, No. 5, p. 1824-1840. DOI:10.3758/s13428-017-0951-1
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Discussion of the synthetic data papers published in the previous issue
Drechsler, J. (2016): Discussion of the synthetic data papers published in the previous issue. In: Statistical Journal of the IAOS, Vol. 32, No. 2, p. 271-274. DOI:10.3233/SJI-161001
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Beat the heap: An imputation strategy for valid inferences from rounded income data
Drechsler, J. & Kiesl, H. (2016): Beat the heap: An imputation strategy for valid inferences from rounded income data. In: Journal of Survey Statistics and Methodology, Vol. 4, No. 1, p. 22-42. DOI:10.1093/jssam/smv032
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MI double feature: Multiple imputation to address nonresponse and rounding errors in income questions
Drechsler, J., Kiesl, H. & Speidel, M. (2015): MI double feature: Multiple imputation to address nonresponse and rounding errors in income questions. In: Austrian Journal of Statistics, Vol. 44, No. 2, p. 59-71. DOI:10.17713/ajs.v44i2.77
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Generating synthetic geocoding information for public release
Hu, J. & Drechsler, J. (2015): Generating synthetic geocoding information for public release. In: S. A. Europäische Kommission (Hrsg.) (2015): NTTS - Conferences on New Techniques and Technologies for Statistics. Brussels, 9-13 March 2015. Proceedings, p. 56-59.
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Multiple imputation of multilevel missing data - rigor vs. simplicity
Drechsler, J. (2015): Multiple imputation of multilevel missing data - rigor vs. simplicity. In: Journal of educational and behavioral statistics, Vol. 40, No. 1, p. 69-95. DOI:10.3102/1076998614563393
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Synthetic longitudinal business databases for international comparisons
Drechsler, J. & Vilhuber, L. (2014): Synthetic longitudinal business databases for international comparisons. In: J. Domingo-Ferrer (Hrsg.) (2014): Privacy in statistical databases 2014 : UNESCO Chair in Data Privacy, International Conference, PSD 2014, Ibiza, Spain, September 17-19, 2014. Proceedings (Lecture notes in computer science, 8744), p. 243-252. DOI:10.1007/978-3-319-11257-2_19
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A first step towards a German SynLBD
Drechsler, J. & Vilhuber, L. (2014): A first step towards a German SynLBD. Constructing a German longitudinal business database. In: Statistical Journal of the IAOS, Vol. 30, No. 2, p. 137-142. DOI:10.3233/SJI-140812
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Disclosure risk from factor scores
Bleninger, P., Drechsler, J. & Ronning, G. (2014): Disclosure risk from factor scores. In: Journal of official statistics, Vol. 30, No. 1, p. 107-122. DOI:10.2478/jos-2014-0006
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Beat the heap - an imputation strategy for valid inferences from rounded income data
Drechsler, J. & Kiesl, H. (2014): Beat the heap - an imputation strategy for valid inferences from rounded income data. (IAB-Discussion Paper 02/2014), Nürnberg, 26 p.
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Evaluating the potential of differential privacy mechanisms for census data
Soria-Comas, J. & Drechsler, J. (2013): Evaluating the potential of differential privacy mechanisms for census data. (UNECE Working paper), New York, 12 p.
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Replicating the synthetic LBD with German establishment data
Vilhuber, L. & Drechsler, J. (2013): Replicating the synthetic LBD with German establishment data. (Labor Dynamics Institute. Working Paper), 6 p.
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Generating useful test data for complex linked employer-employee datasets
Dorner, M., Drechsler, J. & Jacobebbinghaus, P. (2012): Generating useful test data for complex linked employer-employee datasets. In: J. Domingo-Ferrer & I. Tinnirello (Hrsg.) (2012): Privacy in statistical databases (Lecture notes in computer science, 7556), p. 165-178.
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Combining synthetic data with subsampling to create public use microdata files for large scale surveys
Drechsler, J. & Reiter, J. (2012): Combining synthetic data with subsampling to create public use microdata files for large scale surveys. In: Survey Methodology, Vol. 38, No. 1, p. 73-79.
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Remote Access
Ronning, G., Bleninger, P., Drechsler, J. & Gürke, C. (2011): Remote Access. Eine Welt ohne Mikrodaten?? (FDZ-Arbeitspapier 33), Wiesbaden, 59 p.
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An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets
Drechsler, J. & Reiter, J. (2011): An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. In: Europäische Kommission (Hrsg.) (2011): Proceedings of the Eurostat Conference on New Techniques and Technologies for Statistics (NTTS) 2011, Brussels, p. 1-12.
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An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets
Drechsler, J. & Reiter, J. (2011): An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. In: Computational Statistics and Data Analysis, Vol. 55, No. 12, p. 3232-3243. DOI:10.1016/j.csda.2011.06.006
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Improved variance estimation for fully synthetic datasets
Drechsler, J. (2011): Improved variance estimation for fully synthetic datasets. (Joint UNECE/Eurostat work session on statistical data confidentiality 2011. Working paper 18), New York, 13 p.
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Disclosure risk from factor scores in a remote access environment
Bleninger, P., Drechsler, J. & Ronning, G. (2011): Disclosure risk from factor scores in a remote access environment. (Joint UNECE/Eurostat work session on statistical data confidentiality 2011. Working paper 02), New York, 14 p.
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Remote data access and the risk of disclosure from linear regression
Bleninger, P., Drechsler, J. & Ronning, G. (2011): Remote data access and the risk of disclosure from linear regression. In: Statistics and operations research transactions (SORT) No. Special Issue, p. 7-24.
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New data dissemination approaches in old Europe
Drechsler, J. (2012): New data dissemination approaches in old Europe. Synthetic datasets for a German establishment survey. In: Journal of applied statistics, Vol. 39, No. 2, p. 243-265. DOI:10.1080/02664763.2011.584523
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Generating multiply imputed synthetic datasets : theory and implementation. Gerhard-Fürst-Award
Drechsler, J. (2011): Erzeugung synthetischer Datensätze durch multiple Imputation. Theorie und Implementierung in der Praxis. Gerhard-Fürst-Preis. In: Wirtschaft und Statistik No. 4, p. 402-407.
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Methodenreport: Synthetische Scientific-Use-Files der Welle 2007 des IAB-Betriebspanels
Drechsler, J. (2011): Methodenreport: Synthetische Scientific-Use-Files der Welle 2007 des IAB-Betriebspanels. (FDZ-Methodenreport 01/2011 (de)), Nürnberg, 19 p.
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Remote data access and the risk of disclosure from linear regression
Bleninger, P., Drechsler, J. & Ronning, G. (2011): Remote data access and the risk of disclosure from linear regression. An empirical study. In: J. Domingo-Ferrer & E. Magkos (Hrsg.) (2011): Privacy in statistical databases : UNESCO Chair in Data Privacy, International Conference, PSD 2010, Corfu, Greece, September 22-24, 2010. Proceedings (Lecture notes in computer science, 6344), p. 220-233. DOI:10.1007/978-3-642-15838-4_20
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Multiple imputation of missing values in the wave 2007 of the IAB Establishment Panel
Drechsler, J. (2010): Multiple imputation of missing values in the wave 2007 of the IAB Establishment Panel. (IAB-Discussion Paper 06/2010), Nürnberg, 29 p.
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Using support vector machines for generating synthetic datasets
Drechsler, J. (2011): Using support vector machines for generating synthetic datasets. In: J. Domingo-Ferrer & E. Magkos (Hrsg.) (2011): Privacy in statistical databases : UNESCO Chair in Data Privacy, International Conference, PSD 2010, Corfu, Greece, September 22-24, 2010. Proceedings (Lecture notes in computer science, 6344), p. 148-161. DOI:10.1007/978-3-642-15838-4
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Remote Access
Ronning, G., Bleninger, P., Drechsler, J. & Gürke, C. (2010): Remote Access. Eine Welt ohne Mikrodaten?? (IAW-Diskussionspapiere 66), Tübingen, 64 p.
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Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
Reiter, J. & Drechsler, J. (2010): Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality. In: Statistica Sinica, Vol. 20, No. 1, p. 405-421.
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Sampling with synthesis
Drechsler, J. & Reiter, J. (2010): Sampling with synthesis. A new approach for releasing public use census microdata. In: Journal of the American Statistical Association, Vol. 105, No. 492, p. 1347-1357. DOI:10.1198/jasa.2010.ap09480
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Multiple imputation in practice
Drechsler, J. (2011): Multiple imputation in practice. A case study using a complex German establishment survey. In: Advances in statistical analysis, Vol. 95, No. 1, p. 1-26. DOI:10.1007/s10182-010-0136-z
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Disclosure control in business data
Drechsler, J. (2009): Disclosure control in business data. Experiences with multiply imputed synthetic datasets for the German IAB Establishment Survey. In: Europäische Kommission (Hrsg.) (2009): Proceedings of the Eurostat Conference on New Techniques and Technologies for Statistics (NTTS), 2009, Brussels, p. 1-10.
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Synthetic datasets for the German IAB Establishment Panel
Drechsler, J. (2009): Synthetic datasets for the German IAB Establishment Panel. (Joint UNECE/Eurostat Work Session on Statistical Data Confidentiality 2009. Working paper 10), New York, 13 p.
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ESSNET-SDC deliverable report on synthetic data files
Domingo-Ferrer, J., Drechsler, J. & Polettini, S. (2009): ESSNET-SDC deliverable report on synthetic data files. The Hague, 32 p.
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Disclosure risk and data utility for partially synthetic data
Drechsler, J. & Reiter, J. (2009): Disclosure risk and data utility for partially synthetic data. An empirical study using the German IAB Establishment Survey. In: Journal of official statistics, Vol. 25, No. 4, p. 589-603.
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Far from normal: Multiple imputation of missing values in a German establishment survey
Drechsler, J. (2009): Far from normal: Multiple imputation of missing values in a German establishment survey. (United Nations, Economic Commission for Europe. Working paper 21), New York, 13 p.
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Estimation of vacancies by NACE and ISCO at disaggregated regional level
Kettner, A., Drechsler, J., Rebien, M., Schmidt, K., Smerdjieva, M., Stops, M. & Vogler-Ludwig, K. (2007): Estimation of vacancies by NACE and ISCO at disaggregated regional level. (IAB-Bibliothek 310), Nürnberg, 197 p.
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Evaluating different approaches for multiple imputation under linear constraints
Drechsler, J. & Raghunathan, T. (2008): Evaluating different approaches for multiple imputation under linear constraints. (United Nations, Economic Commission for Europe. Working paper 25), New York, 12 p.
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Comparing fully and partially synthetic datasets for statistical disclosure control in the German IAB Establishment Panel
Drechsler, J., Bender, S. & Rässler, S. (2008): Comparing fully and partially synthetic datasets for statistical disclosure control in the German IAB Establishment Panel. In: Transactions on Data Privacy, Vol. 1, No. 3, p. 105-130.
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Accounting for intruder uncertainty due to sampling when estimating identification disclosure risks in partially synthetic data
Drechsler, J. & Reiter, J. (2008): Accounting for intruder uncertainty due to sampling when estimating identification disclosure risks in partially synthetic data. In: J. Domingo-Ferrer & Y. Saygin (Hrsg.) (2008): Privacy in statistical databases : UNESCO Chair in Data Privacy International Conference, PSD 2008, Istanbul, Turkey, September 24-26, 2008. Proceedings (Lecture notes in computer science, 5262), p. 227-238.
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Does convergence really matter?
Drechsler, J. & Rässler, S. (2008): Does convergence really matter? In: Shalabh & C. Heumann (Hrsg.) (2008): Recent advances in linear models and related areas : essays in honour of Helge Toutenburg (Statistical theory and methods, 15).
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A new approach for disclosure control in the IAB Establishment Panel
Drechsler, J., Dundler, A., Bender, S., Rässler, S. & Zwick, T. (2008): A new approach for disclosure control in the IAB Establishment Panel. Multiple imputation for a better data access. In: Advances in statistical analysis, Vol. 92, No. 4, p. 439-458. DOI:10.1007/s10182-008-0090-1
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A new approach for disclosure control in the IAB Establishment Panel
Drechsler, J., Dundler, A., Bender, S., Rässler, S. & Zwick, T. (2007): A new approach for disclosure control in the IAB Establishment Panel. Multiple imputation for a better data access. (IAB-Discussion Paper 11/2007), Nürnberg, 31 p.
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Comparing fully and partially synthetic data sets for statistical disclosure control in the German IAB Establishment Panel
Drechsler, J., Bender, S. & Rässler, S. (2007): Comparing fully and partially synthetic data sets for statistical disclosure control in the German IAB Establishment Panel. (United Nations, Economic Commission for Europe. Working paper 11), New York, 8 p.
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Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
Reiter, J. & Drechsler, J. (2007): Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality. (IAB-Discussion Paper 20/2007), Nürnberg, 26 p.
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A new approach for disclosure control in the IAB Establishment Panel
Bender, S., Drechsler, J., Dundler, A., Rässler, S. & Zwick, T. (2006): A new approach for disclosure control in the IAB Establishment Panel. Multiple imputation for a better data access. (United Nations, Economic Commission for Europe. Working paper 18), New York, 18 p.
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Estimation of vacancies by NACE and ISCO on disaggregated regional level
Kettner, A., Drechsler, J., Rebien, M., Schmidt, K., Semerdjiva, M., Stops, M. & Vogler-Ludwig, K. (2006): Estimation of vacancies by NACE and ISCO on disaggregated regional level. Nürnberg, 101 p., Anhang.
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A new approach for disclosure control in the IAB Establishment Panel
Drechsler, J., Dundler, A. & Rässler, S. (2006): A new approach for disclosure control in the IAB Establishment Panel. Multiple imputation for a better data access. In: (2006): Proceedings of privacy in statistical data bases.