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Joerg Drechsler

Personal Details

First Name:Joerg
Middle Name:
Last Name:Drechsler
Suffix:
RePEc Short-ID:pdr86
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Affiliation

Institut für Arbeitsmarkt- und Berufsforschung (IAB)

Nürnberg, Germany
http://www.iab.de/
RePEc:edi:iabbbde (more details at EDIRC)

Research output

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Jump to: Working papers Articles Chapters

Working papers

  1. Jörg Drechsler & James Bailie, 2024. "The Complexities of Differential Privacy for Survey Data," NBER Working Papers 32905, National Bureau of Economic Research, Inc.
  2. Jorg Drechsler & Lars Vilhuber, 2014. "A First Step Towards A German Synlbd: Constructing A German Longitudinal Business Database," Working Papers 14-13, Center for Economic Studies, U.S. Census Bureau.
  3. Drechsler, Jörg & Kiesl, Hans, 2014. "Beat the heap - an imputation strategy for valid inferences from rounded income data," IAB-Discussion Paper 201402, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  4. Gerd Ronning & Philipp Bleninger & Jörg Drechsler & Christopher Gürke, 2010. "Remote Access – Eine Welt ohne Mikrodaten??," IAW Discussion Papers 66, Institut für Angewandte Wirtschaftsforschung (IAW).
  5. Drechsler, Jörg, 2010. "Multiple imputation of missing values in the wave 2007 of the IAB Establishment Panel," IAB-Discussion Paper 201006, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  6. Reiter, Jerome P. & Drechsler, Jörg, 2007. "Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality," IAB-Discussion Paper 200720, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  7. Drechsler, Jörg & Dundler, Agnes & Bender, Stefan & Rässler, Susanne & Zwick, Thomas, 2007. "A new approach for disclosure control in the IAB Establishment Panel : multiple imputation for a better data access," IAB-Discussion Paper 200711, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

Articles

  1. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
  2. Drechsler Jörg & Ronning Gerd & Bleninger Philipp, 2014. "Disclosure Risk from Factor Scores," Journal of Official Statistics, Sciendo, vol. 30(1), pages 107-122, March.
  3. Jörg Drechsler, 2012. "New data dissemination approaches in old Europe -- synthetic datasets for a German establishment survey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 243-265, April.
  4. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
  5. Jörg Drechsler, 2011. "Multiple imputation in practice—a case study using a complex German establishment survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 1-26, March.
  6. Drechsler, Jörg & Reiter, Jerome P., 2010. "Sampling With Synthesis: A New Approach for Releasing Public Use Census Microdata," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1347-1357.

Chapters

  1. Jörg Drechsler & James Bailie, 2024. "The Complexities of Differential Privacy for Survey Data," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Jorg Drechsler & Lars Vilhuber, 2014. "A First Step Towards A German Synlbd: Constructing A German Longitudinal Business Database," Working Papers 14-13, Center for Economic Studies, U.S. Census Bureau.

    Cited by:

    1. Miranda, Javier & Lars Vilhuber, 2014. "Looking Back On Three Years Of Using The Synthetic Lbd Beta," Working Papers 14-11, Center for Economic Studies, U.S. Census Bureau.

  2. Drechsler, Jörg & Kiesl, Hans, 2014. "Beat the heap - an imputation strategy for valid inferences from rounded income data," IAB-Discussion Paper 201402, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    Cited by:

    1. Tatjana Miljkovic & Ying-Ju Chen, 2021. "A new computational approach for estimation of the Gini index based on grouped data," Computational Statistics, Springer, vol. 36(3), pages 2289-2311, September.
    2. Sangeetha Ann & Meilan Jiang & Toshiyuki Yamamoto, 2019. "Influence Area of Transit-Oriented Development for Individual Delhi Metro Stations Considering Multimodal Accessibility," Sustainability, MDPI, vol. 11(16), pages 1-23, August.
    3. Speidel, Matthias & Drechsler, Jörg & Jolani, Shahab, 2018. "R package hmi: a convenient tool for hierarchical multiple imputation and beyond," IAB-Discussion Paper 201816, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. Sangeetha Ann & Meilan Jiang & Ghasak Ibrahim Mothafer & Toshiyuki Yamamoto, 2019. "Examination on the Influence Area of Transit-Oriented Development: Considering Multimodal Accessibility in New Delhi, India," Sustainability, MDPI, vol. 11(9), pages 1-20, May.

  3. Drechsler, Jörg, 2010. "Multiple imputation of missing values in the wave 2007 of the IAB Establishment Panel," IAB-Discussion Paper 201006, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    Cited by:

    1. Schlitte, Friso, 2010. "Local human capital, segregation by skill, and skill-specific employment growth," HWWI Research Papers 1-32, Hamburg Institute of International Economics (HWWI).
    2. Michael Weinhardt & Alexia Meyermann & Stefan Liebig & Jürgen Schupp, 2016. "The Linked Employer-Employee Study of the Socio-Economic Panel (SOEP-LEE): Project Report," SOEPpapers on Multidisciplinary Panel Data Research 829, DIW Berlin, The German Socio-Economic Panel (SOEP).
    3. Breunig, Christoph & Kummer, Michael & Ohnemus, Jörg & Viete, Steffen, 2016. "IT outsourcing and firm productivity: Eliminating bias from selective missingness in the dependent variable," ZEW Discussion Papers 16-092, ZEW - Leibniz Centre for European Economic Research.

  4. Reiter, Jerome P. & Drechsler, Jörg, 2007. "Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality," IAB-Discussion Paper 200720, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    Cited by:

    1. Jörg Höhne, 2008. "Anonymisierungsverfahren für Paneldaten," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 2(3), pages 259-275, October.
    2. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 33-58, December.
    3. Jan Pablo Burgard & Jan-Philipp Kolb & Hariolf Merkle & Ralf Münnich, 2017. "Synthetic data for open and reproducible methodological research in social sciences and official statistics," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 233-244, December.

  5. Drechsler, Jörg & Dundler, Agnes & Bender, Stefan & Rässler, Susanne & Zwick, Thomas, 2007. "A new approach for disclosure control in the IAB Establishment Panel : multiple imputation for a better data access," IAB-Discussion Paper 200711, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    Cited by:

    1. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
    2. Andrés F. Barrientos & Alexander Bolton & Tom Balmat & Jerome P. Reiter & John M. de Figueiredo & Ashwin Machanavajjhala & Yan Chen & Charles Kneifel & Mark DeLong, 2017. "A Framework for Sharing Confidential Research Data, Applied to Investigating Differential Pay by Race in the U. S. Government," NBER Working Papers 23534, National Bureau of Economic Research, Inc.
    3. Maurice Brandt & Dirk Oberschachtsiek & Ramona Pohl, 2008. "Neue Datenangebote in den Forschungsdatenzentren – Betriebs- und Unternehmensdaten im Längsschnitt –," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 2(3), pages 193-207, October.
    4. Gerd Ronning, 2014. "Vertraulichkeit und Verfügbarkeit von Mikrodaten," IAW Discussion Papers 101, Institut für Angewandte Wirtschaftsforschung (IAW).
    5. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    6. Jörg Höhne, 2008. "Anonymisierungsverfahren für Paneldaten," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 2(3), pages 259-275, October.
    7. Ulrich Kaiser & Joachim Wagner, 2008. "Neue Möglichkeiten zur Nutzung vertraulicher amtlicher Personen‐ und Firmendaten," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 9(3), pages 329-349, August.
    8. Reiter, Jerome P. & Drechsler, Jörg, 2007. "Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality," IAB-Discussion Paper 200720, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    9. M. Jahangir Alam & Benoit Dostie & Jorg Drechsler & Lars Vilhuber, 2020. "Applying Data Synthesis for Longitudinal Business Data across Three Countries," Papers 2008.02246, arXiv.org.
    10. Loong Bronwyn & Rubin Donald B., 2017. "Multiply-Imputed Synthetic Data: Advice to the Imputer," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1005-1019, December.
    11. Jerome P. Reiter, 2009. "Using Multiple Imputation to Integrate and Disseminate Confidential Microdata," International Statistical Review, International Statistical Institute, vol. 77(2), pages 179-195, August.
    12. Stefan Liebig, 2009. "Organizational Data," RatSWD Working Papers 67, German Data Forum (RatSWD).
    13. Jan Pablo Burgard & Jan-Philipp Kolb & Hariolf Merkle & Ralf Münnich, 2017. "Synthetic data for open and reproducible methodological research in social sciences and official statistics," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 233-244, December.

Articles

  1. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.

    Cited by:

    1. Davide Vidotto & Jeroen K. Vermunt & Katrijn van Deun, 2018. "Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 511-539, October.
    2. Chun Wang & Gongjun Xu & Xue Zhang, 2019. "Correction for Item Response Theory Latent Trait Measurement Error in Linear Mixed Effects Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 673-700, September.
    3. Kristian Kleinke, 2017. "Multiple Imputation Under Violated Distributional Assumptions: A Systematic Evaluation of the Assumed Robustness of Predictive Mean Matching," Journal of Educational and Behavioral Statistics, , vol. 42(4), pages 371-404, August.
    4. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2023. "Handling Missing Data in Cross-Classified Multilevel Analyses: An Evaluation of Different Multiple Imputation Approaches," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 454-489, August.
    5. Messner, Wolfgang, 2024. "Exploring multilevel data with deep learning and XAI: The effect of personal-care advertising spending on subjective happiness," International Business Review, Elsevier, vol. 33(1).
    6. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2016. "Multiple Imputation of Multilevel Missing Data," SAGE Open, , vol. 6(4), pages 21582440166, October.
    7. Xiao Tan & Leah Ruppanner & David Maume & Belinda Hewitt, 2021. "Do managers sleep well? The role of gender, gender empowerment and economic development," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-18, March.
    8. Speidel, Matthias & Drechsler, Jörg & Jolani, Shahab, 2018. "R package hmi: a convenient tool for hierarchical multiple imputation and beyond," IAB-Discussion Paper 201816, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    9. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2018. "Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs," Journal of Educational and Behavioral Statistics, , vol. 43(3), pages 316-353, June.
    10. Kristian Kleinke & Jost Reinecke & Cornelia Weins, 2021. "The development of delinquency during adolescence: a comparison of missing data techniques revisited," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 877-895, June.

  2. Drechsler Jörg & Ronning Gerd & Bleninger Philipp, 2014. "Disclosure Risk from Factor Scores," Journal of Official Statistics, Sciendo, vol. 30(1), pages 107-122, March.

    Cited by:

    1. Gerd Ronning, 2014. "Vertraulichkeit und Verfügbarkeit von Mikrodaten," IAW Discussion Papers 101, Institut für Angewandte Wirtschaftsforschung (IAW).

  3. Jörg Drechsler, 2012. "New data dissemination approaches in old Europe -- synthetic datasets for a German establishment survey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 243-265, April.

    Cited by:

    1. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
    2. Gerd Ronning, 2014. "Vertraulichkeit und Verfügbarkeit von Mikrodaten," IAW Discussion Papers 101, Institut für Angewandte Wirtschaftsforschung (IAW).
    3. Javier Miranda & Lars Vilhuber, 2016. "Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics," Working Papers 16-10, Center for Economic Studies, U.S. Census Bureau.
    4. Jorg Drechsler & Lars Vilhuber, 2014. "A First Step Towards A German Synlbd: Constructing A German Longitudinal Business Database," Working Papers 14-13, Center for Economic Studies, U.S. Census Bureau.
    5. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    6. Hang J. Kim & Jerome P. Reiter & Alan F. Karr, 2018. "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 63-82, January.

  4. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.

    Cited by:

    1. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
    2. Joshua Snoke & Gillian M. Raab & Beata Nowok & Chris Dibben & Aleksandra Slavkovic, 2018. "General and specific utility measures for synthetic data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 663-688, June.
    3. Stefan Wimmer & Robert Finger, 2023. "A note on synthetic data for replication purposes in agricultural economics," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(1), pages 316-323, February.
    4. James Jackson & Robin Mitra & Brian Francis & Iain Dove, 2022. "Using saturated count models for user‐friendly synthesis of large confidential administrative databases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1613-1643, October.
    5. Jordan C. Stanley & Evan S. Totty, 2024. "Synthetic Data and Social Science Research: Accuracy Assessments and Practical Considerations from the SIPP Synthetic Beta," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.
    6. Hang J. Kim & Jerome P. Reiter & Alan F. Karr, 2018. "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 63-82, January.
    7. Daniel Manrique‐Vallier & Jingchen Hu, 2018. "Bayesian non‐parametric generation of fully synthetic multivariate categorical data in the presence of structural zeros," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 635-647, June.
    8. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).

  5. Jörg Drechsler, 2011. "Multiple imputation in practice—a case study using a complex German establishment survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 1-26, March.

    Cited by:

    1. Christian Aßmann & Ariane Würbach & Solange Goßmann & Ferdinand Geissler & Anika Bela, 2017. "Nonparametric Multiple Imputation for Questionnaires with Individual Skip Patterns and Constraints: The Case of Income Imputation in the National Educational Panel Study," Sociological Methods & Research, , vol. 46(4), pages 864-897, November.
    2. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
    3. Morehart, Mitch & Milkove, Dan & Xu, Yang, 2014. "Multivariate Farm Debt Imputation in the Agricultural Resource Management Survey (ARMS)," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169401, Agricultural and Applied Economics Association.
    4. Seiler, Christian & Heumann, Christian, 2013. "Microdata imputations and macrodata implications: Evidence from the Ifo Business Survey," Economic Modelling, Elsevier, vol. 35(C), pages 722-733.
    5. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    6. Hübler, Olaf, 2013. "Methods in empirical economics - a selective review with applications," Hannover Economic Papers (HEP) dp-513, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    7. Juana Sanchez & Sydney Noelle Kahmann, 2017. "R&D, Attrition and Multiple Imputation in BRDIS," Working Papers 17-13, Center for Economic Studies, U.S. Census Bureau.
    8. Martin, Eisele & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," MPRA Paper 57666, University Library of Munich, Germany.
    9. Drechsler Jörg & Ronning Gerd & Bleninger Philipp, 2014. "Disclosure Risk from Factor Scores," Journal of Official Statistics, Sciendo, vol. 30(1), pages 107-122, March.

  6. Drechsler, Jörg & Reiter, Jerome P., 2010. "Sampling With Synthesis: A New Approach for Releasing Public Use Census Microdata," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1347-1357.

    Cited by:

    1. Ron S. Jarmin & John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Nathan Goldschlag & Michael B. Hawes & Sallie Ann Keller & Daniel Kifer & Philip Leclerc & Jerome P. Reiter & Rolando A. Rodrígue, 2023. "An in-depth examination of requirements for disclosure risk assessment," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(43), pages 2220558120-, October.
    2. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
    3. Schneider, Matthew J. & Jagpal, Sharan & Gupta, Sachin & Li, Shaobo & Yu, Yan, 2017. "Protecting customer privacy when marketing with second-party data," International Journal of Research in Marketing, Elsevier, vol. 34(3), pages 593-603.
    4. Mittag, Nikolas, 2016. "Correcting for Misreporting of Government Benefits," IZA Discussion Papers 10266, Institute of Labor Economics (IZA).
    5. Javier Miranda & Lars Vilhuber, 2016. "Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics," Working Papers 16-10, Center for Economic Studies, U.S. Census Bureau.
    6. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    7. Nikolas Mittag, 2013. "A Method Of Correcting For Misreporting Applied To The Food Stamp Program," Working Papers 13-28, Center for Economic Studies, U.S. Census Bureau.
    8. Loong Bronwyn & Rubin Donald B., 2017. "Multiply-Imputed Synthetic Data: Advice to the Imputer," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1005-1019, December.
    9. Wieringa, Jaap & Kannan, P.K. & Ma, Xiao & Reutterer, Thomas & Risselada, Hans & Skiera, Bernd, 2021. "Data analytics in a privacy-concerned world," Journal of Business Research, Elsevier, vol. 122(C), pages 915-925.
    10. Hang J. Kim & Jerome P. Reiter & Alan F. Karr, 2018. "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 63-82, January.

Chapters

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More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 3 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (1) 2010-02-27
  2. NEP-EUR: Microeconomic European Issues (1) 2014-03-22
  3. NEP-IPR: Intellectual Property Rights (1) 2024-10-14

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