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。
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。
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。
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