DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Jeongyoun | ko |
dc.contributor.author | Lee, Myung Hee | ko |
dc.contributor.author | Lee, Jung Ae | ko |
dc.date.accessioned | 2021-06-02T02:50:10Z | - |
dc.date.available | 2021-06-02T02:50:10Z | - |
dc.date.created | 2021-06-02 | - |
dc.date.created | 2021-06-02 | - |
dc.date.issued | 2019-01 | - |
dc.identifier.citation | JOURNAL OF APPLIED STATISTICS, v.46, no.1, pp.13 - 29 | - |
dc.identifier.issn | 0266-4763 | - |
dc.identifier.uri | http://hdl.handle.net/10203/285422 | - |
dc.description.abstract | Despite the popularity of high dimension, low sample size data analysis, there has not been enough attention to the sample integrity issue, in particular, a possibility of outliers in the data. A new outlier detection procedure for data with much larger dimensionality than the sample size is presented. The proposed method is motivated by asymptotic properties of high-dimensional distance measures. Empirical studies suggest that high-dimensional outlier detection is more likely to suffer from a swamping effect rather than a masking effect, thus yields more false positives than false negatives. We compare the proposed approaches with existing methods using simulated data from various population settings. A real data example is presented with a consideration on the implication of found outliers. | - |
dc.language | English | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Distance-based outlier detection for high dimension, low sample size data | - |
dc.type | Article | - |
dc.identifier.wosid | 000449972800002 | - |
dc.identifier.scopusid | 2-s2.0-85044385520 | - |
dc.type.rims | ART | - |
dc.citation.volume | 46 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 13 | - |
dc.citation.endingpage | 29 | - |
dc.citation.publicationname | JOURNAL OF APPLIED STATISTICS | - |
dc.identifier.doi | 10.1080/02664763.2018.1452901 | - |
dc.contributor.localauthor | Ahn, Jeongyoun | - |
dc.contributor.nonIdAuthor | Lee, Myung Hee | - |
dc.contributor.nonIdAuthor | Lee, Jung Ae | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Centroid distance | - |
dc.subject.keywordAuthor | HDLSS | - |
dc.subject.keywordAuthor | high-dimensional asymptotics | - |
dc.subject.keywordAuthor | maximal data piling distance | - |
dc.subject.keywordAuthor | multiple outliers | - |
dc.subject.keywordPlus | GEOMETRIC REPRESENTATION | - |
dc.subject.keywordPlus | MODEL | - |
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