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Inference for Multivariate Regression Model based on Synthetic Data generated using Plug-in Sampling
Inference for Multivariate Regression Model based on Synthetic Data generated using Plug-in Sampling
RRS2018-02
Abstract
In this paper, the authors derive the likelihood-based exact inference for singly and multiply imputed synthetic data in the context of a multivariate regression model. The synthetic data are generated via the Plug-in Sampling method, where the unknown parameters in the model are set equal to the observed values of their point estimators based on the original data, and synthetic data are drawn from this estimated version of the model. Simulation studies are carried out in order to confirm the theoretical results. In case multiple synthetic datasets are permissible, the authors provide an exact test procedure and compare their results with the asymptotic results of Reiter (2005). An application using U.S. 2000 Current Population Survey data is discussed. Furthermore, properties of the proposed methodology are evaluated in scenarios where some of the conditions that were used to derive the methodology do not hold.
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Statistical Research Reports and Studies
Page Last Revised - October 28, 2021
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