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http://dx.doi.org/10.5351/KJAS.2018.31.1.029

Imputation method for missing data based on clustering and measure of property  

Kim, Sunghyun (Department of Biomedicine.Health Science, The Catholic University of Korea)
Kim, Dongjae (Department of Biomedicine.Health Science, The Catholic University of Korea)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.1, 2018 , pp. 29-40 More about this Journal
Abstract
There are various reasons for missing values when collecting data. Missing values have some influence on the analysis and results; consequently, various methods of processing missing values have been studied to solve the problem. It is thought that the later point of view may be affected by the initial time point value in the repeated measurement data. However, in the existing method, there was no method for the imputation of missing values using this concept. Therefore, we proposed a new missing value imputation method in this study using clustering in initial time point of the repeated measurement data and the measure of property proposed by Kim and Kim (The Korean Communications in Statistics, 30, 463-473, 2017). We also applied the Monte Carlo simulations to compare the performance of the established method and suggested methods in repeated measurement data.
Keywords
imputation of missing value; clustering; measure of property; initial time point;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Choi, Y. and Jeong, K. (2003). Multivariate Analysis using SAS and Its Application, Free Academy, Seoul.
2 Jeon, C. (2012). Data Mining Techniques and Applications, Hanarae Academy, Seoul.
3 Kang, S. (2013). Medical Statistics for New Medicine Development, Free Academy, Seoul.
4 Kim, H. and Kim, D. (2017). Imputation method for missing data based on measure of property, The Korean Communications in Statistics, 30, 463-473.
5 Lee, S. (2008). Conjugation plan of proc MI, Industrial Science Research, 26, 35-41.
6 Shin, S. (2010). Model-based cluster analysis of missing data considering outlier, Korea University Graduate School.
7 Ward, J. H. (1963). Hierarchical groupings to optimize an objective function, Journal of the American Statistical Association, 58, 234-244.