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초록
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.
키워드
regression outlier; robust residual; clustering; masking; swamping; regression outlier; robust residual; clustering; masking; swamping
- 제목
- Identification of Regression Outliers Based on Clustering of LMS-residual Plots
- 제목 (타언어)
- Identification of Regression Outliers Based on Clustering of LMS-residual Plots
- 저자
- 김부용; 오미현
- 발행일
- 2004-12
- 권
- 11
- 호
- 3
- 페이지
- 485 ~ 494