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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제8권 제3호
- 발행연도
- 2006.1
- 수록면
- 895 - 900 (6page)
이용수
초록· 키워드
오류제보하기
A method of detecting outliers using local influence is suggested for multivariate regression with linear constraints. It allows assessment of simultaneous perturbation affecting all the data. We consider a perturbation scheme of case-weights. This perturbation scheme simultaneously perturbs all the cases. The direction vector associated with the largest curvature of the curve at the null point provides information about outliers that cause a great change in the likelihood displacement. Observations corresponding to the component of the direction vector associated with the largest curvature that has substantially larger absolute value than the others are potential outliers. A numerical example is provided for illustration.
목차
등록된 정보가 없습니다.
참고문헌 (5)
참고문헌 신청
/ 1986 / Assessment of local influence Journal of the Royal Statistical Society : 133 ~ 169
/ 1989 / Detection of influential observations in multivariate regression Journal of Applied Statistics : 25 ~ 37
/ 1995 / Local influence in multivariate regression 24 : 1271 ~ 1278
/ 1997 / Matrix Analysis for Statistics
/ 2005 / Detection of outliers in multivariate regression using plug-in method 7 : 1117 ~ 1124
/ 1989 / Detection of influential observations in multivariate regression Journal of Applied Statistics : 25 ~ 37
/ 1995 / Local influence in multivariate regression 24 : 1271 ~ 1278
/ 1997 / Matrix Analysis for Statistics
/ 2005 / Detection of outliers in multivariate regression using plug-in method 7 : 1117 ~ 1124
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