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Contributed paper Likelihood-Based Finite Sample Inference for Synthetic Data Based on Exponential Model
, 2013
"... Likelihood-based finite sample inference based on synthetic data under the exponential model is developed in this paper. Two distinct synthetic data gen-eration scenarios are considered, one based on posterior predictive sampling, and the other based on plug-in sampling. It is demonstrated that vali ..."
Abstract
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Likelihood-based finite sample inference based on synthetic data under the exponential model is developed in this paper. Two distinct synthetic data gen-eration scenarios are considered, one based on posterior predictive sampling, and the other based on plug-in sampling. It is demonstrated that valid in-ference can be drawn in both scenarios, even for a singly imputed synthetic dataset. The usual combination rules for drawing inference under multiple syn-thetic datasets are discussed in the context of likelihood-based data analysis. Disclaimer: This article is released to inform interested parties of ongoing research and to encourage discussion. The views expressed are those of the authors and not necessarily those of the U.S. Census Bureau.
Noise Multiplication for Statistical Disclosure Control of Extreme Values in Log-normal Regression Samples
"... Statistical agencies must control disclosure risk when releasing data to the public. If income data on individuals or businesses are released, it could be possible to match ex-tremely large values to specific individuals or businesses that are known to be wealthy, especially if some additional infor ..."
Abstract
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Statistical agencies must control disclosure risk when releasing data to the public. If income data on individuals or businesses are released, it could be possible to match ex-tremely large values to specific individuals or businesses that are known to be wealthy, especially if some additional information is available on the same units in the dataset.
