Graduate Students Seminar

Speaker: Abhishek Guin

Title

Bayesian Analysis of Singly Imputed Synthetic Data under the Multivariate Normal Model

Abstract

We develop Bayesian inference based on singly imputed partially synthetic data, when the original data are derived from a multivariate normal model. We assume that the synthetic data are generated by using two methods: plug-in sampling, where unknown parameters in the data model are set equal to observed values of their point estimators based on the original data, and synthetic data are drawn from this estimated version of the model; and, posterior predictive sampling, where an imputed posterior distribution of the unknown parameters is used to generate a posterior draw, which in turn is plugged in the original model to produce synthetic data. The multiple imputation case is addressed briefly as well. Simulation results are presented to demonstrate how the proposed methodology performs compared to the theoretical predictions.