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

被引：0
作者：
Riccardo Borgoni
Ann Berrington
机构：
[1] University of Milano-Bicocca,Department of Statistics
[2] University of Southampton,Division of Social Statistics, Southampton Statistical Sciences Research Institute
来源：
Quality & Quantity | 2013年 / 47卷
关键词：
Missing data; Sequential imputation; Classification tree; 1970 British Birth Cohort;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：1991 / 2008
页数：17
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