Evaluating a sequential tree-based procedure for multivariate imputation of complex missing data structures

Riccardo Borgoni and Ann Berrington

Quality & Quantity: International Journal of Methodology, 2013, vol. 47, issue 4, 2008 pages

Abstract: 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. Copyright Springer Science+Business Media B.V. 2013

Keywords: Missing data; Sequential imputation; Classification tree; 1970 British Birth Cohort (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s11135-011-9638-3

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