CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST 2014, 30(6) 598-606 DOI:      ISSN: 1001-4268 CN: 31-1256

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

Zhang Xiaoqin, Kang Ju, Jing Wenjun

School of Mathematics Science, Shanxi University

Abstract��

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.

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