With multiple imputation, these imperfections and information about the processes underlying them can be taken into account.
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In combination single imputation of missing data with multiple imputation, a new missing data imputation—KNNMI is proposed.
综合数据缺失值的单一填补和多重填补方法，提出一种新的信用指标缺失值填补方法—KNNMI。
Conclusion The multiple-imputation method was the best technique to handle with the missing values in the schistosomiasis surveillance data.
结论多重填充技术较为适合处理该资料中缺失比例较少的缺失值。
The paper introduces multiple imputation (mi) for missing data in stratified random sampling, and discusses the ordinary method of mi with ignorable nonresponse, and illustrates the essential steps.
介绍分层随机抽样条件下多重插补法处理缺失数据的基本思想，分析可忽略无回答的分层随机抽样建立多重插补的常用方法，并通过实例加以说明。
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