Sequential regression multiple imputation for incomplete multivariate data using Markov Chain Monte Carlo

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dc.contributor.author Lacerda, M. en_US
dc.contributor.author Ardington, Cally en_US
dc.contributor.author Leibbrandt, Murray en_US
dc.date.accessioned 2012-12-03T12:05:37Z
dc.date.available 2012-12-03T12:05:37Z
dc.date.issued 2007-12 en_US
dc.identifier.uri http://hdl.handle.net/11090/41
dc.description.abstract This paper discusses the theoretical background to handling missing data in a multivariate context. Earlier methods for dealing with item non-response are reviewed, followed by an examination of some of the more modern methods and, in particular, multiple imputation. One such technique, known as sequential regression multivariate imputation, which employs a Markov chain Monte Carlo algorithm is described and implemented. It is demonstrated that distributional convergence is rapid and only a few imputations are necessary in order to produce accurate point estimates and preserve multivariate relationships, whilst adequately accounting for the uncertainty introduced by the imputation procedure. It is further shown that lower fractions of missing data and the inclusion of relevant covariates in the imputation model are desirable in terms of bias reduction. en_US
dc.publisher Southern Africa Labour and Development Research Unit en_US
dc.subject Missing data
dc.subject Monte Carlo
dc.subject Multiple imputation
dc.subject Markov chain Monte Carlo
dc.title Sequential regression multiple imputation for incomplete multivariate data using Markov Chain Monte Carlo en_US


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