Significance tests for multi-component estimands from multiply imputed, synthetic microdata


Journal Article

To limit the risks of disclosures when releasing data to the public, it has been suggested that statistical agencies release multiply imputed, synthetic microdata. For example, the released microdata can be fully synthetic, comprising random samples of units from the sampling frame with simulated values of variables. Or, the released microdata can be partially synthetic, comprising the units originally surveyed with some collected values, e.g. sensitive values at high risk of disclosure or values of key identifiers, replaced with multiple imputations. This article presents inferential methods for synthetic data for multi-component estimands, in particular procedures for Wald and likelihood ratio tests. The performance of the procedures is illustrated with simulation studies. © 2004 Elsevier B.V. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Reiter, JP

Published Date

  • May 1, 2005

Published In

Volume / Issue

  • 131 / 2

Start / End Page

  • 365 - 377

International Standard Serial Number (ISSN)

  • 0378-3758

Digital Object Identifier (DOI)

  • 10.1016/j.jspi.2004.02.003

Citation Source

  • Scopus