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Multiple imputation: an alternative to top coding for statistical disclosure control

dc.contributor.authorAn, Dien_US
dc.contributor.authorLittle, Roderick J. A.en_US
dc.date.accessioned2010-06-01T20:56:37Z
dc.date.available2010-06-01T20:56:37Z
dc.date.issued2007-10en_US
dc.identifier.citationAn, Di; Little, Roderick J. A. (2007). "Multiple imputation: an alternative to top coding for statistical disclosure control." Journal of the Royal Statistical Society: Series A (Statistics in Society) 170(4): 923-940. <http://hdl.handle.net/2027.42/74038>en_US
dc.identifier.issn0964-1998en_US
dc.identifier.issn1467-985Xen_US
dc.identifier.urihttps://hdl.handle.net/2027.42/74038
dc.format.extent788260 bytes
dc.format.extent3109 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Ltden_US
dc.rights2007 Royal Statistical Societyen_US
dc.subject.otherConfidentialityen_US
dc.subject.otherDisclosure Protectionen_US
dc.subject.otherMultiple Imputationen_US
dc.titleMultiple imputation: an alternative to top coding for statistical disclosure controlen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumUniversity of Michigan, Ann Arbor, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/74038/1/j.1467-985X.2007.00492.x.pdf
dc.identifier.doi10.1111/j.1467-985X.2007.00492.xen_US
dc.identifier.sourceJournal of the Royal Statistical Society: Series A (Statistics in Society)en_US
dc.identifier.citedreferenceDempster, A. P., Laird, N. M. and Rubin, D. B. ( 1977 ) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Statist. Soc. B, 39, 1 – 38.en_US
dc.identifier.citedreferenceFuller, W. A. ( 1993 ) Masking procedures for microdata disclosure limitation. J. Off. Statist., 2, 383 – 406.en_US
dc.identifier.citedreferenceLittle, R. J. A. ( 1993 ) Statistical analysis of masked data. J. Off. Statist., 9, 407 – 426.en_US
dc.identifier.citedreferenceLittle, R. J., Liu, F. and Raghunathan, T. ( 2004 ) Statistical disclosure techniques based on multiple imputation. In Applied Bayesian Modeling and Causal Inference from Incomplete-data Perspectives ( eds A. Gelman and X.-L. Meng ), pp. 141 – 152. New York: Wiley.en_US
dc.identifier.citedreferenceLittle, R. J. A. and Rubin, D. B. ( 2002 ) Statistical Analysis with Missing Data. New York: Wiley.en_US
dc.identifier.citedreferenceReiter, J. P. ( 2003 ) Inference for partially synthetic, public use microdata sets. Surv. Methodol., 29, 181 – 188.en_US
dc.identifier.citedreferenceReiter, J. P. ( 2005a ) Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study. J. R. Statist. Soc. A, 168, 185 – 205.en_US
dc.identifier.citedreferenceReiter, J. P. ( 2005b ) Significance tests for multi-component estimands from multiply-imputed, synthetic microdata. J. Statist. Planng Inf., 131, 365 – 377.en_US
dc.identifier.citedreferenceRiskin, C., Zhao, R. and Li, S. ( 2000 ). Political Economy Research Institute, University of Massachusetts, Amherst. (Available from http://webapp.icpsr.umich.edu/cocoon/ICPSR-STUDY/03012.xml. )en_US
dc.identifier.citedreferenceR Project ( 2007 ) The R Project for Statistical Computing. (See http://www.r-project.org/.)en_US
dc.identifier.citedreferenceRubin, D. B. ( 1993 ) Satisfying confidentiality constraints through use of synthetic multiply-imputed microdata. J. Off. Statist., 9, 461 – 468.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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