Sequential regression multiple imputation for incomplete multivariate data using Markov Chain Monte Carlo
| dc.creator | Lacerda, M. | |
| dc.creator | Ardington, Cally | |
| dc.creator | Leibbrandt, Murray | |
| dc.date | 2012-12-03T12:05:37Z | |
| dc.date | 2012-12-03T12:05:37Z | |
| dc.date | 2007-12 | |
| dc.date.accessioned | 2015-05-28T10:05:04Z | |
| dc.date.available | 2015-05-28T10:05:04Z | |
| dc.date.issued | 2015-05-28 | |
| dc.description | 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. | |
| dc.identifier | http://hdl.handle.net/11090/41 | |
| dc.identifier.ris | TY - Report DA - 2015-05-28 DB - OpenUCT DP - University of Cape Town KW - Missing data KW - Monte Carlo KW - Multiple imputation KW - Markov chain Monte Carlo LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Sequential regression multiple imputation for incomplete multivariate data using Markov Chain Monte Carlo TI - Sequential regression multiple imputation for incomplete multivariate data using Markov Chain Monte Carlo UR - http://hdl.handle.net/11090/41 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11090/41 | |
| dc.language.iso | eng | |
| dc.publisher | Southern Africa Labour and Development Research Unit | |
| dc.publisher.department | SALDRU | en_ZA |
| dc.publisher.faculty | Faculty of Commerce | en_ZA |
| dc.publisher.institution | University of Cape Town | |
| 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 | |
| dc.type | Report | |
| uct.type.publication | Research | en_ZA |
| uct.type.resource | SALDRU Report | en_ZA |