[Abowd, J.M. and L. Vilhuber. 2008. “How Protective are Synthetic Data?” In Privacy in Statistical Databases, edited by J. Domingo-Ferrer and V. Yucel, 239–246. New York: Springer.10.1007/978-3-540-87471-3_20]Search in Google Scholar
[Drechsler, J., A. Dundler, S. Bender, S. Rässler, and T. Zwick. 2008. “A New Approach for Disclosure Control in the IAB Establishment Panel – Multiple Imputation for a Better Data Access.” Advances in Statistical Analysis 92: 439–458. Doi: http://dx.doi.org/10.1007/s10182-008-0090-1.10.1007/s10182-008-0090-1]Open DOISearch in Google Scholar
[Drechsler, J. and J.P. Reiter. 2010. “Sampling with Synthesis: a New Approach for Releasing Public Use Census Microdata.” Journal of the American Statistical Association 105: 1347–1357. Doi: http://dx.doi.org/10.1198/jasa.2010.ap09480.10.1198/jasa.2010.ap09480]Open DOISearch in Google Scholar
[Duncan, G.T. and D. Lambert. 1989. “The Risk of Disclosure for Microdata.” Journal of Business and Economic Statistics 7: 207–217. Doi: http://dx.doi.org/10.1080/07350015.1989.10509729.10.1080/07350015.1989.10509729]Open DOISearch in Google Scholar
[Harel, O. and J.L. Schafer. 2003. “Multiple Imputation in Two Stages.” In Proceedings of Federal Committee on Statistical Methodology 2003 Conference, November 17–19, 2003, Washington DC. Available at: http://fcsm.sites.usa.gov/files/2014/05/2003FCSM_Harel.pdf (accessed August 2017).]Search in Google Scholar
[Karr, A.F., C.N. Kohnen, A. Oganian, J.P. Reiter, and A.P. Sanil. 2006. “A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality.” The American Statistician 60: 224–232. Doi: http://dx.doi.org/10.1198/000313006X124640.10.1198/000313006X124640]Open DOISearch in Google Scholar
[Li, F., M. Baccini, F. Mealli, E.Z. Zell, C.E. Frangakis, and D.B. Rubin. 2014. “Multiple Imputation by Ordered Monotone Blocks, with Applications to the Anthrax Vaccine Adsorbed Trial.” Journal of Computational and Graphical Statistics 23: 877–892. Doi: http://dx.doi.org/10.1080/10618600.2013.826583.10.1080/10618600.2013.826583]Open DOISearch in Google Scholar
[Meng, X.L. 1994. “Multiple-Imputation Inferences with Uncongenial Sources of Input.” Statistical Science 9: 538–558. Doi: http://dx.doi.org/10.1214/ss/1177010269.10.1214/ss/1177010269]Open DOISearch in Google Scholar
[Neyman, J. 1934. “On Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection (with Discussion).” Journal of the Royal Statistical Society 97: 558–625.10.2307/2342192]Open DOISearch in Google Scholar
[Raghunathan, T.E. and D.B. Rubin. 2000. “Bayesian Multiple Imputation to Preserve Confidentiality in Public-Use Data Sets.” In Proceedings of ISBA 2000 – The Sixth World Meeting of the International Society for Bayesian Analysis, Crete, May 2000.]Search in Google Scholar
[Raghunathan, T.E., J.M. Lepkowski, J. van Hoewyk, and P. Solenberger. 2001. “A Multivariate Technique for Multiply Imputing Missing Values Using a Series of Regression Models.” Survey Methodology 27: 85–96.]Search in Google Scholar
[Raghunathan, T.E., J.P. Reiter, and D.B. Rubin. 2003. “Multiple Imputation for Statistical Disclosure Limitation.” Journal of Official Statistics 19: 1–16.]Search in Google Scholar
[Reiter, J.P. 2002. “Satisfying Disclosure Restrictions with Synthetic Datasets.” Journal of Official Statistics 18: 531–543.]Search in Google Scholar
[Reiter, J.P. 2003. “Inference for Partially Synthetic, Public Use Microdata Sets.” Survey Methodology 29: 181–189.]Search in Google Scholar
[Reiter, J.P. 2005a. “Releasing Multiply Imputed Synthetic Public Use Microdata: An Illustration and Empirical Study.” Journal of the Royal Statistical Society, Series A 168: 185–205. Doi: http://dx.doi.org/10.1111/j.1467-985X.2004.00343.x.10.1111/j.1467-985X.2004.00343.x]Open DOISearch in Google Scholar
[Reiter, J.P. 2005b. “Significance Tests for Multi-Component Estimands from Multiply Imputed, Synthetic Microdata.” Journal of Statistical Planning and Inference 131: 365–377. Doi: http://dx.doi.org/10.1016/j.jspi.2004.02.003.10.1016/j.jspi.2004.02.003]Open DOISearch in Google Scholar
[Reiter, J.P. 2009. “Multiple Imputation for Disclosure Limitation: Future Research Challenges.” Journal of Privacy and Confidentiality 1: 223–233.10.29012/jpc.v1i2.575]Search in Google Scholar
[Reiter, J.P., T.E. Raghunathan, and S. Kinney. 2006. “The Importance of Modelling the Sampling Design in Multiple Imputation for Missing Data.” Survey Methodology 32: 143–149.]Search in Google Scholar
[Reiter, J.P. and R. Mitra. 2009. “Estimating Risks of Identification and Disclosure in Partially Synthetic Data.” Journal of Privacy and Confidentiality 1: 99–110.10.29012/jpc.v1i1.567]Search in Google Scholar
[Reiter, J.P. and J. Drechsler. 2010. “Two Stage Multiple Imputation to Protect Confidentiality.” Statistica Sinica 20: 405–422.]Search in Google Scholar
[Reiter, J.P., Q. Wang, and B.E. Zhang. 2014. “Bayesian Estimation of Disclosure Risks for Multiply Imputed, Synthetic Data.” Journal of Privacy and Confidentiality 6: 17–33.10.29012/jpc.v6i1.635]Search in Google Scholar
[Rubin, D.B. 1978. “Multiple Imputation in Sample Surveys.” In Proceedings of the Survey Research Methods Section of the American Statistical Association, 20–34. Alexandria, VA: American Statistical Association, August 14-17, San Diego. Available at: https://ww2.amstat.org/sections/srms/Proceedings/papers/1978_004.pdf (accessed August 2017).]Search in Google Scholar
[Rubin, D.B. 1987. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons, Inc.10.1002/9780470316696]Search in Google Scholar
[Rubin, D.B. 1993. “Discussion: Statistical Disclosure Limitation.” Journal of Official Statistics 9: 461–468.]Search in Google Scholar
[Rubin, D.B. 2003. “Nested Multiple Imputation of NMES via Partially Incompatible MCMC.” Statistica Neerlandica 57: 3–18. Doi: http://dx.doi.org/10.1111/1467-9574.00217.10.1111/1467-9574.00217]Open DOISearch in Google Scholar
[Schafer, J.L. 1997. Analysis of Incomplete Multivariate Data. London: Chapman & Hall.10.1201/9781439821862]Search in Google Scholar
[Shen, Z. 2000. Nested Multiple Imputation. Ph.D. thesis, Harvard University, Dept. of Statistics: Cambridge, MA.]Search in Google Scholar
[Van Buuren, S. and C.G.M. Oudshoorn. 2000. Multivariate Imputation by Chained Equations: MICE v1.0 user’s manual. Leiden: TNO. Available at: http://www.stefvanbuuren.nl/publications/mice%20v1.0%20manual%20tno00038%202000.pdf (accessed september 2017).]Search in Google Scholar
[Woo, M.J., J.P. Reiter, A. Oganian, and A.F. Karr. 2009. “Global Measures of Data Utility for Microdata Masked for Disclosure Limitation.” Journal of Privacy and Confidentiality 1: 111–124.10.29012/jpc.v1i1.568]Search in Google Scholar
[Xie, X. and X.L. Meng. 2014. “Dissecting Multiple Imputation from a Multi-Phase Inference Perspective: What Happens When God’s, Imputer’s and Analyst’s Models are Uncongenial?” Statistica Sinica. Preprint. Doi: http://dx.doi.org/10.5705/ss.2014.067.10.5705/ss.2014.067]Open DOISearch in Google Scholar