Bayesian finite population imputation for data fusion

Statistica Sinica

Volumn 22, Issue 2, 2012, Pages 795-811

  Reiter, Jerome P ^a  

^a Duke University   (United States)

Author keywords

Confidentiality; Disclosure; Matching; Multiple; Sharing; Synthetic

Indexed keywords

EID: 84858433275     PISSN: 10170405     EISSN: None     Source Type: Journal    
DOI: 10.5705/ss.2010.140     Document Type: Article

References (28)

1. Abowd, J. M. and Woodcock, S. D. (2004). Multiply-imputing confidential characteristics and file links in longitudinal linked data. In Privacy in Statistical Databases, (Edited by J. Domingo-Ferrer and V. Torra), 290-297. Springer-Verlag: New York.
2. D'Orazio, M., Di Zio, M. and Scanu, M. (2002). Statistical matching and official statistics. Rivista di Statistica Ufficiale 1, 5-24.
3. D'Orazio, M., Di Zio, M. and Scanu, M. (2006). Statistical Matching: Theory and Practice. Wiley, New York.
4. Drechsler, J. and Reiter, J. P. (2008). Accounting for intruder uncertainty due to sampling when estimating identification disclosure risks in partially synthetic data. In Privacy in Statistical Databases (LNCS 5262), (Edited by J. Domingo-Ferrer and Y. Saygin), 227-238. Springer-Verlag: New York.
5. Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (2004). Bayesian Data Analysis. Chapman and Hall, London.
6. Gilula, Z., McCulloch, R. E. and Rossi, P. E. (2006). A direct approach to data fusion. J. Marketing Res. 43, 73-83. (Pubitemid 43331605)
7. Kadane, J. (2001). Some statistical problems in merging datasets. J. Official Statist. 17, 423-433.
8. Kamakura, W. A. and Wedel, M. (1997). Statistical data fusion for cross-tabulation. J. Marketing Res. 34, 485-498.
9. Kohnen, C. N. and Reiter, J. P. (2009). Multiple imputation for combining confidential data owned by two agencies. J. Roy. Statist. Soc. Ser. A 172, 511-528.
10. Little, R. J. A. (1988). Missing data adjustments in large surveys. J. Bus. Econom. Statist. 6, 287-296.
11. Little, R. J. A. (1993). Statistical analysis of masked data. J. Official Statist. 9, 407-426.
12. Moriarity, C. and Scheuren, F. (2001). Statistical matching: A paradigm for assessing the uncertainty in the procedure. J. Official Statist. 17, 407-422.
13. Moriarity, C. and Scheuren, F. (2003). A note on Rubin's statistical matching using file concatenation with adjusted weights and multiple imputations. J. Bus. Econom. Statist. 21, 65-73. (Pubitemid 36116521)
14. Rässler, S. (2002). Statistical Matching: A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches. Lecture Notes in Statistics 168, Springer: New York.
15. Rässler, S. (2003). A non-iterative Bayesian approach to statistical matching. Statist. Neerlandica 57, 58-74. (Pubitemid 41724438)
16. Rässler, S. (2004). Data fusion: Identification problems, validity, and multiple imputation. Austral. J. Statist. 33, 153-171.
17. Reiter, J. P. (2003). Inference for partially synthetic, public use microdata sets. Survey Methodology 29, 181-189.
18. Reiter, J. P. (2005). Using CART to generate partially synthetic, public use microdata. J. Official Statist. 21, 441-462.
19. Reiter, J. P. (2009). Using multiple imputation to integrate and disseminate confidential microdata. Internat. Statist. Rev. 77, 179-195.
20. Reiter, J. P. and Drechsler, J. (2010). Releasing multiply-imputed, synthetic data generated in two stages to protect confidentiality. Statist. Sinica 20, 405-422.
21. Reiter, J. P. and Mitra, R. (2009). Estimating risks of identification disclosure in partially synthetic data. J. Privacy and Confidentiality 1, 99-110.
22. Reiter, J. P., Raghunathan, T. E. and Kinney, S. K. (2006). The importance of modeling the survey design in multiple imputation for missing data. Survey Methodology 32, 143-150.
23. Rodgers, W. L. (1984). An evaluation of statistical matching. J. Bus. Econom. Statist. 2, 91-102.
24. Rubin, D. B. (1981). The Bayesian bootstrap. Ann. Statist. 9, 130-134.
25. Rubin, D. B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations. J. Bus. Econom. Statist. 4, 87-94.
26. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley, New York.
27. Rubin, D. B. (1993). Discussion: Statistical disclosure limitation. J. Official Statist. 9, 462-468.
28. van der Putten, P., Kok, J. N. and Gupta, A. (2002). Why the information explosion can be bad for data mining, and how data fusion provides a way out. In Proceedings of the Second SIAM International Conference on Data Mining, (Edited by R. L. Grossman, J. Han, V. Kumar, H. Mannila, and R. Motwant), SIAM: Arlington, VA.