Multiple imputation: An alternative to top coding for statistical disclosure control

Journal of the Royal Statistical Society. Series A: Statistics in Society

Volumn 170, Issue 4, 2007, Pages 923-940

  An, Di ^a   Little, Roderick J A ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Confidentiality; Disclosure protection; Multiple imputation

Indexed keywords

EID: 34848822584     PISSN: 09641998     EISSN: 1467985X     Source Type: Journal    
DOI: 10.1111/j.1467-985X.2007.00492.x     Document Type: Article

References (11)

1. Dempster, A. P., Laird, N. M. Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Statist. Soc. B, 39, 1 38.
2. Fuller, W. A. (1993) Masking procedures for microdata disclosure limitation. J. Off. Statist., 2, 383 406.
3. Little, R. J. A. (1993) Statistical analysis of masked data. J. Off. Statist., 9, 407 426.
4. Little, R. J., Liu, F. Raghunathan, T. (2004) Statistical disclosure techniques based on multiple imputation. In Applied Bayesian Modeling and Causal Inference from Incomplete-data Perspectives (eds A. Gelman X.-L. Meng pp. 141 152. New York : Wiley.
5. Little, R. J. A. Rubin, D. B. (2002) Statistical Analysis with Missing Data. New York : Wiley.
6. Reiter, J. P. (2003) Inference for partially synthetic, public use microdata sets. Surv. Methodol., 29, 181 188.
7. Reiter, J. P. (2005a) Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study. J. R. Statist. Soc. A, 168, 185 205.
8. Reiter, J. P. (2005b) Significance tests for multi-component estimands from multiply-imputed, synthetic microdata. J. Statist. Planng Inf., 131, 365 377.
9. Riskin, C., Zhao, R. Li, S. (2000). Political Economy Research Institute, University of Massachusetts, Amherst. (Available from http://webapp.icpsr. umich.edu/cocoon/ICPSR-STUDY/03012.xml.)
10. R Project (2007) The R Project for Statistical Computing. (See http://www.r-project.org/.)
11. Rubin, D. B. (1993) Satisfying confidentiality constraints through use of synthetic multiply-imputed microdata. J. Off. Statist., 9, 461 468.