一种基于Epanechnikov二次核的成分数据缺失值填补法

An Imputation Method for Missing Data in Compositional Based on Epanechnikov Kernel

核函数方法已经被成功的用于各种函数的估计.本文利用核函数的思想,针对缺失数据造成现有的成分数据统计方法失效和k近邻填补法(KNNI)在利用缺失数据的k个近邻估计缺失数据时没有考虑到它们各自不同的贡献,提出了一种基于Epanechnikov二次核的成分数据缺失值填补法(EKI)和对其进行修正后的Epanechnikov核成分数据缺失值填补法(MEKI).实验结果表明,基于修正的Epanechnikov二次核的成分数据缺失值填补法比k近邻填补法能够得到更为准确的估计.

Kernel function method has been successfully used for the estimation of a variety of function. By using the kernel function theory, an imputation method based on Epanechnikov kernel and its modification were proposed to solve the problem that missing data in compositional caused the failures of existing statistical methods and the k-nearest imputation didn＇t consider the different contributions of the k nearest samples when it used them to estimated the missing data. The experimental results illustrate that the modified imputation method based on Epanechnikov kernel get a more accurate estimation than k-nearest imputation for compositional data.