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Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling

RRS2019-06
Martin Klein, Ricardo Moura, and Bimal Sinha
Component ID: #ti1088968147

Abstract

In this paper we consider singly imputed synthetic data generated via plug-in sampling under the multivariate normal model. Based on the observed synthetic dataset, we derive a statistical test for the generalized variance, the sphericity test, a test for independence between two subsets of variables, and a test for the regression of one set of variables on the other. The procedures are based on finite sample theory. Some simulation studies are presented which confirm that the proposed procedures perform as expected.

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