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

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  • Klein, Martin
  • Sinha, Bimal

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.

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  • Klein, Martin & Sinha, Bimal, 2015. "Likelihood-based inference for singly and multiply imputed synthetic data under a normal model," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 168-175.
    • Handle: RePEc:eee:stapro:v:105:y:2015:i:c:p:168-175
    DOI: 10.1016/j.spl.2015.06.003
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    References listed on IDEAS

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    1. Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011. "Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database," International Statistical Review, International Statistical Institute, vol. 79(3), pages 362-384, December.
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    1. Martin Klein & Ricardo Moura & Bimal Sinha, 2021. "Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 273-287, May.

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