A comparison of imputation methods using nonlinear models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyein | - |
dc.contributor.author | Song, Juwon | - |
dc.date.accessioned | 2021-09-01T11:00:57Z | - |
dc.date.available | 2021-09-01T11:00:57Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2019-08 | - |
dc.identifier.issn | 1225-066X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/64026 | - |
dc.description.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. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | KOREAN STATISTICAL SOC | - |
dc.title | A comparison of imputation methods using nonlinear models | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Song, Juwon | - |
dc.identifier.doi | 10.5351/KJAS.2019.32.4.543 | - |
dc.identifier.wosid | 000497647100005 | - |
dc.identifier.bibliographicCitation | KOREAN JOURNAL OF APPLIED STATISTICS, v.32, no.4, pp.543 - 559 | - |
dc.relation.isPartOf | KOREAN JOURNAL OF APPLIED STATISTICS | - |
dc.citation.title | KOREAN JOURNAL OF APPLIED STATISTICS | - |
dc.citation.volume | 32 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 543 | - |
dc.citation.endPage | 559 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002500670 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordAuthor | missing data | - |
dc.subject.keywordAuthor | imputation | - |
dc.subject.keywordAuthor | nonlinear model | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.