An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Woodcock, Simon D. & Benedetto, Gary, 2009.
"Distribution-preserving statistical disclosure limitation,"
Computational Statistics & Data Analysis,
Elsevier, vol. 53(12), pages 4228-4242, October.
- Woodcock, Simon & Benedetto, Gary, 2006. "Distribution-Preserving Statistical Disclosure Limitation," MPRA Paper 155, University Library of Munich, Germany.
- Simon D. Woodcock & Gary Benedetto, 2006. "Distribution Preserving Statistical Disclosure Limitation," Longitudinal Employer-Household Dynamics Technical Papers 2006-04, Center for Economic Studies, U.S. Census Bureau.
- Simon D. Woodcock & Gary Benedetto, 2007. "Distribution-Preserving Statistical Disclosure Limitation," Discussion Papers dp07-15, Department of Economics, Simon Fraser University.
- Iacus, Stefano M. & Porro, Giuseppe, 2007. "Missing data imputation, matching and other applications of random recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 773-789, October.
- Reiter, Jerome P. & Raghunathan, Trivellore E., 2007. "The Multiple Adaptations of Multiple Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1462-1471, December.
- Jerome P. Reiter, 2005. "Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 185-205.
- Drechsler, JÃ¶rg & 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.
- 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].
- Shim, Jooyong & Sohn, Insuk & Kim, Sujong & Lee, Jae Won & Green, Paul E. & Hwang, Changha, 2009. "Selecting marker genes for cancer classification using supervised weighted kernel clustering and the support vector machine," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1736-1742, March.
- Choi, Hosik & Yeo, Donghwa & Kwon, Sunghoon & Kim, Yongdai, 2011. "Gene selection and prediction for cancer classification using support vector machines with a reject option," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1897-1908, May.
- Skinner, Chris & Shlomo, Natalie, 2008. "Assessing Identification Risk in Survey Microdata Using Log-Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 989-1001.
- 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.
- John M. Abowd & Simon D. Woodcock, 2004. "Multiply-Imputing Confidential Characteristics and File Links in Longitudinal Linked Data," Longitudinal Employer-Household Dynamics Technical Papers 2004-04, Center for Economic Studies, U.S. Census Bureau.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- repec:jss:jstsof:v:074:i11 is not listed on IDEAS
More about this item
KeywordsCensus Confidentiality Disclosure Imputation Microdata Synthetic;
StatisticsAccess and download statistics
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:55:y:2011:i:12:p:3232-3243. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/csda .
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.