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。
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