关键词: 多重填补, 缺失数据, 核函数, 非参

Abstract: In the preprocessing of data mining，data missing is one of the most common problems in data preprocessing.Quite frequently，the author have little priori knowledge about distribution of the data we want to mine.Under this condition，non-parametric regression provides an effective approach to handle the data missing.Accordingly，a new kernel-based non-parametric Multiple Imputation（MI） algorithm is proposed，under two missing mechanisms，MAR（Missing At Random） and MCAR（Missing Completely At Random）.Experiments over simulation data show that our algorithm performs much better than the traditional NORM method，in the coverage of confidence interval，the interval length，and the relative efficiency.

Key words: Multiple Imputation（MI）, missing values, kernel function, non-parametric