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).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1007/s11135-011-9638-3
ISSNs: 0033-5177 (print)
1573-7845 (electronic)
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
ePrint ID: 201035
Accepted Date and Publication Date:
December 2011Made publicly available
Date Deposited: 27 Oct 2011 13:41
Last Modified: 31 Mar 2016 13:45
Centre for Population Change: Understanding Population Change in the 21st Century
Funded by: ESRC National Centre for Research Methods (RES-625-28-0001)
Led by: Jane Cecelia Falkingham
1 January 2009 to 31 December 2013
URI: http://eprints.soton.ac.uk/id/eprint/201035

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