On the use of adaptive nearest neighbors for missing value imputation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jhun, Myoungshic | - |
dc.contributor.author | Jeong, Hyeong Chul | - |
dc.contributor.author | Koo, Ja-Yong | - |
dc.date.accessioned | 2021-09-09T06:25:38Z | - |
dc.date.available | 2021-09-09T06:25:38Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2007 | - |
dc.identifier.issn | 0361-0918 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/123096 | - |
dc.description.abstract | A popular nonparametric treatment of missing value imputation uses methods based on k-nearest neighbors, where the number k of nearest neighbors is fixed without any consideration of the local features of missing values. This article proposes an alternative imputation method based on adaptive nearest neighbors, which takes into account the local features of the data. The proposed method adapts the number of neighbors in imputing the missing values according to the location of the missing values. Efficiency evaluation is then gauged through simulation studies using both simulated and real data. It is shown that the proposed method has distinct advantages over the imputation method based on k-nearest neighbors. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS INC | - |
dc.subject | GENE SELECTION | - |
dc.subject | MICROARRAYS | - |
dc.title | On the use of adaptive nearest neighbors for missing value imputation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Koo, Ja-Yong | - |
dc.identifier.doi | 10.1080/03610910701569069 | - |
dc.identifier.scopusid | 2-s2.0-36549010118 | - |
dc.identifier.wosid | 000251715100010 | - |
dc.identifier.bibliographicCitation | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.36, no.6, pp.1275 - 1286 | - |
dc.relation.isPartOf | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
dc.citation.title | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
dc.citation.volume | 36 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1275 | - |
dc.citation.endPage | 1286 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | GENE SELECTION | - |
dc.subject.keywordPlus | MICROARRAYS | - |
dc.subject.keywordAuthor | adaptive choice | - |
dc.subject.keywordAuthor | imputation | - |
dc.subject.keywordAuthor | k-nearest neighbors | - |
dc.subject.keywordAuthor | local features | - |
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