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A comparison of imputation methods using nonlinear models

Authors
Kim, HyeinSong, Juwon
Issue Date
8월-2019
Publisher
KOREAN STATISTICAL SOC
Keywords
missing data; imputation; nonlinear model
Citation
KOREAN JOURNAL OF APPLIED STATISTICS, v.32, no.4, pp.543 - 559
Indexed
KCI
Journal Title
KOREAN JOURNAL OF APPLIED STATISTICS
Volume
32
Number
4
Start Page
543
End Page
559
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/64026
DOI
10.5351/KJAS.2019.32.4.543
ISSN
1225-066X
Abstract
Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.
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정경대학 (통계학과)
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