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Identification of Regression Outliers Based on Clustering of LMS-residual PlotsIdentification of Regression Outliers Based on Clustering of LMS-residual Plots

Other Titles
Identification of Regression Outliers Based on Clustering of LMS-residual Plots
Authors
김부용오미현
Issue Date
Dec-2004
Publisher
한국통계학회
Keywords
regression outlier; robust residual; clustering; masking; swamping; regression outlier; robust residual; clustering; masking; swamping
Citation
Communications for Statistical Applications and Methods, v.11, no.3, pp 485 - 494
Pages
10
Journal Title
Communications for Statistical Applications and Methods
Volume
11
Number
3
Start Page
485
End Page
494
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/15966
ISSN
2287-7843
Abstract
An algorithm is proposed to identify multiple outliers in linear regression. It is based on the clustering of residuals from the least median of squares estimation. A cut-height criterion for the hierarchical cluster tree is suggested, which yields the optimal clustering of the regression outliers. Comparisons of the efectiveness of the procedures are performed on the basis of the clasic data and artificial data sets, and it is shown that the proposed algorithm is superior to the one that is based on the least squares estimation. In particular, the algorithm deals very well with the masking and swamping effects while the other does not.
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