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。
RESULTS: The multiple imputation method imputed missing values of the crossover design and generated valid statistical inferences.
结果：多重填补的方法可用于交叉设计中缺失数据的填补并得出正确的统计推断。
Results The multiple imputation method can impute missing values of the crossover design and generate valid statistical inferences.
结果多重填补的方法可用于交叉设计中缺失数据的填补并得出正确的统计推断。
Then, variance estimates of single imputation, simplified calculation of multiple imputation and imputation using response probability are studied.
重点讨论了单一插补的方差估计与多重插补的简化计算以及使用回答概率的单一插补等。
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。
This paper introduces several imputation methods: those methods include: Deductive imputation, Mean value imputation, Randomized imputation Regression method and Multiple imputation.
本文介绍的插补方法有：演绎估计，均值插补，随机插补，回归插补和多重插补。
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。
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。
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。
应用推荐