Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

Ricardo Moura, Bimal Sinha, Carlos A. Coelho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

Original languageEnglish
Title of host publicationApplied Mathematics and Computer Science
Subtitle of host publicationProceedings of the 1st International Conference on Applied Mathematics and Computer Science
PublisherAmerican Institute of Physics Inc.
Number of pages7
Volume1836
ISBN (Electronic)978-0-7354-1506-5
DOIs
Publication statusPublished - 5 Jun 2017
Event1st International Conference on Applied Mathematics and Computer Science, ICAMCS 2017 - Rome, Italy
Duration: 27 Jan 201729 Jan 2017

Conference

Conference1st International Conference on Applied Mathematics and Computer Science, ICAMCS 2017
CountryItaly
CityRome
Period27/01/1729/01/17

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Moura, R., Sinha, B., & Coelho, C. A. (2017). Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling. In Applied Mathematics and Computer Science: Proceedings of the 1st International Conference on Applied Mathematics and Computer Science (Vol. 1836). [020065] American Institute of Physics Inc.. https://doi.org/10.1063/1.4982005