Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1151781 | Statistics & Probability Letters | 2015 | 8 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Martin Klein, Bimal Sinha,