Extending the reach of sequential regression multiple imputation

[摘要] English: The purpose of this thesis is twofold. Firstly, it reviews a signi cant portion of literatureconcerning multiple imputation and, in particular, sequential regression multipleimputation, and summarises this information, thereby allowing a reader to gain in-depthknowledge of this researcheld. Secondly, the thesis delves into one particular novel topicin sequential regression multiple imputation. The latter objective, of course, is not trulypossible without the former, since the deeper the review of multiple imputation, the morelikely it will be to identify and solve pressing concerns in the sequential regression multipleimputation sub eld.The literature review will show that there is room in imputation research for work ona robust model for the sequential regression multiple imputation algorithm. This thesispays particular attention to this robust model, formulating its estimation procedure withinthe context of sequential regression multiple imputation of continuous data, attempting todiscover a statistic that would show when to use the robust model over the regular Normalspeci cation, and then implementing the robust model in another estimation algorithmthat might allow for better imputation of ordinal data.This thesis contributes to `extending the reach of sequential regression multiple imputation'in two ways. Firstly, it is my wish for users of public data sets, particularly in SouthAfrica, to become familiar with the (now internationally standard) topics presented intherst half of this thesis. The only way to start publicising sequential regression multipleimputation in South Africa is to lay out the evidence for and against this procedurein a logical manner, so that any reader of this thesis might be able to understand theprocedures for analysing multiply imputed data, or tackle one of the many research problemsuncovered in this text. In this way, this thesis will extend the reach of sequentialregression multiple imputation to many more South African researchers. Secondly, byworking on a new robust model for use in the sequential regression multiple imputationalgorithm, this thesis strengthens the sequential regression multiple imputation algorithmby extending its reach to incomplete data that is not necessarily Normally distributed, beit due to heavy tails, or inherent skewness, or both.

[发布机构] University of the Free State