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The authors derive likelihood-based exact inference methods for themultivariate regression model, for singly imputed synthetic data generated viaPosterior Predictive Sampling (PPS) and for multiply imputed synthetic datagenerated via a newly proposed sampling method, which the authors callFixed-Posterior Predictive Sampling (FPPS). In the single imputation case, ourproposed FPPS method concurs with the usual Posterior Predictive Sampling (PPS)method, thus filling the gap in the existing literature where inferentialmethods are only available for multiple imputation. Simulation studies comparethe results obtained with those for the exact test procedures under the Plug-inSampling method, obtained by the same authors. Measures of privacy arediscussed and compared with the measures derived for the Plug-in Samplingmethod. An application using U.S.\ 2000 Current Population Survey data isdiscussed.

Inference for Multivariate Regression Model based on synthetic data  generated under Fixed-Posterior Predictive Sampling: comparison with Plug-in  Sampling