  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.
