Multiple imputation: an alternative to top coding for statistical disclosure control
AbstractTop coding of extreme values of variables like income is a common method of statistical disclosure control, but it creates problems for the data analyst. The paper proposes two alternative methods to top coding for statistical disclosure control that are based on multiple imputation. We show in simulation studies that the multiple-imputation methods provide better inferences of the publicly released data than top coding, using straightforward multiple-imputation methods of analysis, while maintaining good statistical disclosure control properties. We illustrate the methods on data from the 1995 Chinese household income project. Copyright 2007 Royal Statistical Society.
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Bibliographic InfoArticle provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).
Volume (Year): 170 (2007)
Issue (Month): 4 ()
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- Jenkins, Stephen P. & Burkhauser, Richard V. & Feng, Shuaizhang & Larrimore, Jeff, 2009.
"Measuring Inequality Using Censored Data: A Multiple Imputation Approach,"
IZA Discussion Papers
4011, Institute for the Study of Labor (IZA).
- Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2009. "Measuring Inequality Using Censored Data: A Multiple Imputation Approach," Discussion Papers of DIW Berlin 866, DIW Berlin, German Institute for Economic Research.
- Stephen Jenkins & Richard Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2009. "Measuring Inequality Using Censored Data: A Multiple Imputation Approach," Working Papers 09-05, Center for Economic Studies, U.S. Census Bureau.
- Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2009. "Measuring inequality using Censored data: A multiple imputation approach," Working Papers 108, ECINEQ, Society for the Study of Economic Inequality.
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