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Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study
Author(s) -
Reiter Jerome P.
Publication year - 2005
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2004.00343.x
Subject(s) - microdata (statistics) , synthetic data , imputation (statistics) , computer science , confidentiality , econometrics , public use , survey data collection , population , missing data , data science , data mining , statistics , artificial intelligence , mathematics , machine learning , census , medicine , computer security , environmental health , political science , law
Summary. The paper presents an illustration and empirical study of releasing multiply imputed, fully synthetic public use microdata. Simulations based on data from the US Current Population Survey are used to evaluate the potential validity of inferences based on fully synthetic data for a variety of descriptive and analytic estimands, to assess the degree of protection of confidentiality that is afforded by fully synthetic data and to illustrate the specification of synthetic data imputation models. Benefits and limitations of releasing fully synthetic data sets are discussed.