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。
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.
方法用MI对缺失数据进行填补，用标准的统计程序对填补后的数据集分析，最后用多重填补分析综合各个数据集的统计分析结果。
Methods Using MI to fill in missing data and analyzing the multiply imputed data sets with standard statistical procedure, then combining the statistical inferences with MIANALYZE procedure.
结论多重填补与多重填补分析为处理存在缺失数据的资料提供了有效的策略。
Conclusion mi and MIANALYZE procedures provide a valid strategy for handling data set with missing values.
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.
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。
Results The multiple imputation method can impute missing values of the crossover design and generate valid statistical inferences.
应用推荐