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Journal of Korean Society for Quality Management 1995;23(4): 28-. |
다변량 자료에서 다수 이상치 인식의 절차 |
염준근1, 박종구2, 김종우3 |
1동국대학교 통계학과 2원광대학교 컴퓨터공학과 3제주교육대학교 수학교육과 |
A Procedure for Indentifying Outliers in Multivariate Data |
Joon-Keun Yum1, Jong-Goo Park2, Jong-Woo Kim3 |
1Dept. of Statistics, Dongguk University 2Dept. of Computer Science, Wonkwang University 3Dept. of Mathematics Education, Cheju National University |
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ABSTRACT |
We consider the problem of identifying multiple outliers in linear model. The available regression diagnostic methods often do not succeed in detecting multiple outliers because of the masking and swamping effect. Recently, among the various robust estimator of reducing the effect of outliers, LMS(Least Meadian Square) estimator has been to be a suitable method proposed to expose outliers and leverage points. However, as you know it, the data analysis method with LMS estimator is to be taken the median of the squared residuals in the sample which is extracted the sample space. Then this model causes the trouble, for the number of the chosen sample is nCp, i.e. as the size of sample space n is increasing, the number is increasing fastly. And the covariance matrix may be the singular matrix, so that matrix is approching collinearity. Thus we propose a procedure ELMS for the resampling in LMS method and study the size of the effective elementary set in this algorithm. |
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