Evaluating a sequential tree-based procedure for multivariate imputation of complex missing data structures
Borgoni, Riccardo and Berrington, Ann (2011) Evaluating a sequential tree-based procedure for multivariate imputation of complex missing data structures. Quality & Quantity (doi:10.1007/s11135-011-9638-3).
Item nonresponse in survey data can pose significant problems for social scientists carrying out statistical modeling using a large number of explanatory variables. A number of imputation methods exist but many only deal with univariate imputation, or relatively simple cases of multivariate imputation, often assuming a monotone pattern of missingness. In this paper we evaluate a tree-based approach for multivariate imputation using real data from the 1970 British Cohort Study, known for its complex pattern of nonresponse. The performance of this tree-based approach is compared to mode imputation and a sequential regression based approach within a simulation study.
|Keywords:||missing data, sequential imputation, classification tree, 1970 british birth cohort|
|Subjects:||H Social Sciences > HA Statistics|
|Divisions:||Faculty of Social and Human Sciences > Social Sciences > Social Statistics & Demography
|Date Deposited:||27 Oct 2011 13:41|
|Last Modified:||16 Dec 2011 11:53|
|Contributors:||Borgoni, Riccardo (Author)
Berrington, Ann (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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