Conclusion mi is able to solve a variety of problems in missing data sets and to improve the statistical power, especially with the use of MCMC method, for complicated missing data sets.
结论多重填补方法可以处理有缺失数据资料中的许多普遍问题，可提高统计效率，尤其是MCMC模型在处理复杂的缺失数据上，优势明显。
This method is suitable not only to the complete sample case but also to the general missing data cases.
这种方法既适合于全样本场合又适合于一般缺失数据场合。
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。
Firstly, the method of how to guess the missing data is in detail discussed and the definition as well as the mining method of distance based association rule is given.
首先具体讨论了如何猜测丢失的数据，给出了基于距离的关联规则的定义及挖掘方法。
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。
Compared with the crisp extension matrix, the proposed method has the capability of handling fuzzy representation and tolerating noisy data or missing data.
与清晰情况下相比较，我们所实现的这种算法能够处理模糊数据，对噪音数据和不完整数据有很好的鲁棒性。
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。
Experimental results prove that this method can not produce data missing, and verdict the feasibility and validity of this method.
实验结果验证该方法的可行性和有效性，并证明其不会产生数据损失。
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。
Compared with the existing technologies, the method can enhance the accuracy of identification and overcome the limitation that data traffic cannot be identified because of missing hand-shake packet.
实验表明，对比现有的技术，该方法能提高识别的准确性，克服因未捕获握手包而无法识别数据流的缺陷。
Compared with the existing technologies, the method can enhance the accuracy of identification and overcome the limitation that data traffic cannot be identified because of missing hand-shake packet.
实验表明，对比现有的技术，该方法能提高识别的准确性，克服因未捕获握手包而无法识别数据流的缺陷。
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