Likelihood-based inference for singly and multiply imputed synthetic data under a normal model

Likelihood-based inference for both singly and multiply imputed synthetic data is developed in this paper under a univariate normal model and two distinct data generation scenarios, namely, posterior predictive sampling and plug-in sampling. We show that valid and exact inference can be drawn in both scenarios. Some theoretical issues of multiply imputed datasets under posterior predictive sampling are also pointed out.