One approach to limiting disclosure risk in public-use microdata is to release multiply-imputed, partially synthetic data sets. These are data on actual respondents, but with con dential data replaced by multiply-imputed synthetic values. When imputing confidential values, a mis-specified model can invalidate inferences, because the distribution of synthetic data is determined by the model used to generate them. We present a practical method to generate synthetic values when the imputer has only limited information about the true data generating process. We combine a simple imputation model (such as regression) with a series of density-based transformations to pre- serve the distribution of the con dential data, up to sampling error, on speci ed subdomains. We demonstrate through simulation and a large scale application that our approach preserves important statistical properties of the con dential data, including higher moments, with low disclosure risk.
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Paper provided by Department of Economics, Simon Fraser University in its series Discussion Papers with number
dp07-15.
Length: 39 Date of creation: Sep 2007 Date of revision: Handle: RePEc:sfu:sfudps:dp07-15
Contact details of provider: Postal: Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada Phone: (778)782-3508 Fax: (778)782-5944 Web page: http://www.econ.sfu.ca/ More information through EDIRC
Find related papers by JEL classification: C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics C5 - Mathematical and Quantitative Methods - - Econometric Modeling
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