A Noise-Aware Multiple Imputation Algorithm for Missing Data

Fangfang Li (), Hui Sun, Yu Gu and Ge Yu
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Fangfang Li: School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Hui Sun: School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Yu Gu: School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Ge Yu: School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China

Mathematics, 2022, vol. 11, issue 1, 1-16

Abstract: Missing data is a common and inevitable phenomenon. In practical applications, the datasets usually contain noises for various reasons. Most of the existing missing data imputing algorithms are affected by noises which reduce the accuracy of the imputation. This paper proposes a noise-aware missing data multiple imputation algorithm NPMI in static data. Different multiple imputation models are proposed according to the missing mechanism of data. Secondly, the method to determine the imputation order of multivariablesmissing is given. A random sampling consistency algorithm is proposed to estimate the initial values of the parameters of the multiple imputation model to reduce the influence of noise data and improve the algorithm’s robustness. Experiments on two real datasets and two synthetic datasets verify the accuracy and efficiency of the proposed NPMI algorithm, and the results are analyzed.

Keywords: noise-aware; missing data; multiple imputation; regression prediction; Markov chain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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