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

DOI10.1016/j.jspi.2004.02.003zbMath1061.62201OpenAlexW2050032749MaRDI QIDQ1772679

Jerome P. Reiter

Publication date: 21 April 2005

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2004.02.003

zbMATH Keywords

DisclosureMultiple imputationConfidentialitySignificance testsSynthetic data

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Hypothesis testing in multivariate analysis (62H15) Applications of statistics (62P99)

Related Items (6)

Multiple imputation for sharing precise geographies in public use data ⋮ New data dissemination approaches in old Europe – synthetic datasets for a German establishment survey ⋮ A comparison of posterior simulation and inference by combining rules for multiple imputation ⋮ Selecting the number of imputed datasets when using multiple imputation for missing data and disclosure limitation ⋮ Distribution-preserving statistical disclosure limitation ⋮ Inference for Multivariate Regression Model Based on Synthetic Data Generated Using Plug-in Sampling

Uses Software

• BayesDA

Cites Work

• Releasing Multiply Imputed, Synthetic Public use Microdata: An Illustration and Empirical Study
• Elements of statistical disclosure control
• Performing likelihood ratio tests with multiply-imputed data sets
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