Inference for Multivariate Regression Model based on Synthetic Data generated under Fixed-Posterior Predictive Sampling: Comparison with Plug-in Sampling

Ricardo Moura, Klein Martin, Bimal Sinha, Carlos A. Coelho

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Abstract

The authors derive likelihood-based exact inference methods for the multivariate regression model, for singly imputed synthetic data generated via Posterior Predictive Sampling (PPS) and for multiply imputed synthetic data generated via a newly proposed sampling method, which the authors call Fixed-Posterior Predictive Sampling (FPPS). In the single imputation case, our proposed FPPS method concurs with the usual Posterior Predictive Sampling (PPS) method, thus filling the gap in the existing literature where inferential methods are only available for multiple imputation. Simulation studies compare the results obtained with those for the exact test procedures under the Plug-in Sampling method, obtained by the same authors. Measures of privacy are discussed and compared with the measures derived for the Plug-in Sampling method. An application using U.S. 2000 Current Population Survey data is discussed.
Original languageEnglish
Pages (from-to)155-186
Number of pages32
JournalREVSTAT - STATISTICAL JOURNAL
Volume15
Issue number2
Publication statusPublished - 1 Apr 2017

Keywords

  • finite sample inference
  • maximum likelihood estimation
  • pivotal quantity
  • plug-in sampling
  • statistical disclosure control
  • unbiased estimators

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