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。
The topics enclose the important phases of designing a split questionnaire, and the methods of using the multiple imputation method to deal with the missing data.
重点阐述其设计要点，以及如何利用多重插补方法对缺失数据进行处理。
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。
Objective to compare the three imputation methods of missing values and provide scientific basis for the best imputation methods of missing values for the schistosomiasis surveillance data in China.
目的以全国血吸虫病疫情监测资料为数据来源，比较不同缺失值处理方法对模拟缺失值的处理结果，为确定适用于处理该资料缺失值的方法提供依据。
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。
A imputation method based on Mahalanobis distance was proposed to estimate missing values in the gene expression data.
提出了一种基于马氏距离的填充算法来估计基因表达数据集中的缺失数据。
A imputation method based on Mahalanobis distance was proposed to estimate missing values in the gene expression data.
提出了一种基于马氏距离的填充算法来估计基因表达数据集中的缺失数据。
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