Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Identifying Multiple Leverage Points ad Outliers in Multivariate Linear Models

  • Yoo, Jong-Young (Department of Computer Science and Statistics, Yongin University)
  • Published : 2000.12.01
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Abstract

This paper focuses on the problem of detecting multiple leverage points and outliers in multivariate linear models. It is well known that he identification of these points is affected by masking and swamping effects. To identify them, Rousseeuw(1985) used robust estimators of MVE(Minimum Volume Ellipsoids), which have the breakdown point of 50% approximately. And Rousseeuw and van Zomeren(1990) suggested the robust distance based on MVE, however, of which the computation is extremely difficult when the number of observations n is large. In this study, e propose a new algorithm to reduce the computational difficulty of MVE. The proposed method is powerful in identifying multiple leverage points and outlies and also effective in reducing the computational difficulty of MVE.

Keywords

References

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