Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling
Martin Klein (),
Ricardo Moura () and
Bimal Sinha ()
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Martin Klein: U.S. Food and Drug Administration (FDA), CDER/OTS/OB/DBVIII
Ricardo Moura: Portuguese Navy Research Center (CINAV)
Bimal Sinha: University of Maryland, Baltimore County
Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 1, No 12, 273-287
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.
Keywords: Multivariate normal; Pivotal quantity; Plug-in sampling; Statistical disclosure control; Tests for covariance structure.; Primary 62H15; Secondary 62F03 (search for similar items in EconPapers)
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