Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation

An, D. and Little, R.J.A. (2007). Multiple Imputation: An Alternative to Top Coding for Statistical Disclosure Control. Journal of Royal Statistical Society, Series A, 170, 923-940.10.1111/j.1467-985X.2007.00492.xSearch in Google Scholar

Devroye, L. (1986). Non-Uniform Random Variate Generation: Springer.10.1007/978-1-4613-8643-8Search in Google Scholar

Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2003). Bayesian Data Analysis, (second edition). Chapman & Hall/CRC.Search in Google Scholar

Kim, J. (1986). A Method for Limiting Disclosure in Microdata Based on Random Noise and Transformation. In Proceedings of the American Statistical Association, Section on Survey Research Methods, 303-308.Search in Google Scholar

Kim, J.J. and Winkler, W.E. (1995). Masking Microdata Files. In Proceedings of the American Statistical Association, Section on Survey Research Methods, 114-119.Search in Google Scholar

Kim, J.J. and Winkler, W.E. (2003). Multiplicative Noise for Masking Continuous Data. Statistical Research Division Research Report Series (Statistics #2003-01). U.S. Census Bureau. Available at: www.census.gov/srd/papers/pdf/rrs2003-01.pdf (accessed May 14, 2012).Search in Google Scholar

Klein, M., Mathew, T., and Sinha, B. (2013). A Comparison of Statistical Disclosure Control Methods: Multiple Imputation Versus Noise Multiplication. Center for Statistical Research & Methodology, Research and Methodology Directorate Research Report Series (Statistics #2013-02). U.S. Census Bureau. Available at: www.census.gov/srd/papers/pdf/rrs2013-02.pdf (accessed Jan. 23, 2013).Search in Google Scholar

Klein, M. and Sinha, B. (2013). Statistical Analysis of Noise Multiplied Data Using Multiple Imputation. Center for Statistical Research and Methodology, Research and Methodology Directorate Research Report Series (Statistics #2013-01). U.S. Census Bureau. Available at: www.census.gov/srd/papers/pdf/rrs2013-01.pdf (accessed Jan. 23, 2013).Search in Google Scholar

Little, R.J.A. (1993). Statistical Analysis of Masked Data. Journal of Official Statistics, 9, 407-426.Search in Google Scholar

Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis With Missing Data, (second edition). Wiley.Search in Google Scholar

Meng, X.L. (1994). Multiple-Imputation Inferences with Uncongenial Sources of Input. Statistical Science, 9, 538-558.Search in Google Scholar

Nayak, T., Sinha, B.K., and Zayatz, L. (2011). Statistical Properties of Multiplicative Noise Masking for Confidentiality Protection. Journal of Official Statistics, 27, 527-544.Search in Google Scholar

R Development Core Team (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at: www.R-project.org/.Search in Google Scholar

Raghunathan, T.E., Reiter, J.P., and Rubin, D.B. (2003). Multiple Imputation for Statistical Disclosure Limitation. Journal of Official Statistics, 19, 1-16.Search in Google Scholar

Reiter, J.P. (2003). Inference for Partially Synthetic, Public Use Microdata Sets. Survey Methodology, 29, 181-188.Search in Google Scholar

Reiter, J.P. (2005). Releasing Multiply Imputed, Synthetic Public Use Microdata: An Illustration and Empirical Study. Journal of Royal Statistical Society, Series A, 168, 185-205.10.1111/j.1467-985X.2004.00343.xSearch in Google Scholar

Reiter, J.P. and Raghunathan, T.E. (2007). The Multiple Adaptations of Multiple Imputation. Journal of American Statistical Association, 102, 1462-1471.10.1198/016214507000000932Search in Google Scholar

Robert, C.P. and Casella, G. (2005). Monte Carlo Statistical Methods, (second edition). Springer.Search in Google Scholar

Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys: Wiley.10.1002/9780470316696Search in Google Scholar

Rubin, D.B. (1993). Discussion: Statistical Disclosure Limitation. Journal of Official Statistics, 9, 461-468.Search in Google Scholar

Robins, J.M. and Wang, N. (2000). Inference for Imputation Estimators. Biometrika, 87, 113-124.10.1093/biomet/87.1.113Search in Google Scholar

Sinha, B.K., Nayak, T., and Zayatz, L. (2012). Privacy Protection and Quantile Estimation From Noise Multiplied Data. Sankhya, Series B, 73, 297-315.10.1007/s13571-011-0030-zSearch in Google Scholar

Tanner, M.A. and Wong, W.H. (1987). The Calculation of Posterior Distributions by Data Augmentation (with discussion). Journal of the American Statistical Association, 82, 528-550.10.1080/01621459.1987.10478458Search in Google Scholar

Wang, N. and Robins, J.M. (1998). Large-Sample Theory for Parametric Multiple Imputation Procedures. Biometrika, 85, 935-948. 10.1093/biomet/85.4.935Search in Google Scholar