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Bayesian Analysis of Singly Imputed Synthetic Data under the Multivariate Normal Model
Bayesian Analysis of Singly Imputed Synthetic Data under the Multivariate Normal Model
RRS2023-04
Abstract
We develop appropriate Bayesian procedures to draw inference about the parameters under a multivariate normal model based on synthetic data. We consider two standard forms of synthetic data, generated under plug in sampling method and posterior predictive sampling method. In addition to point estimates of the mean vector and dispersion matrix, Bayesian credible sets for the mean vector and the generalized variance are also provided under both the scenarios. The analysis in the case when some (partial) features are sensitive and need to be hidden is also briefly indicated.
Page Last Revised - July 14, 2023
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