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。
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。
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。
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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