Eigenstructure-based angle for detecting outliers in multivariate data
There are two main reasons that motivate people to detect outliers; the first is the researchers’ intention; see the example of Mr Haldum’s cases in Barnett and Lewis. The second is the effect of outliers on analyses. This article does not differentiate between the various justifications for outlier...
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| Format: | Article |
| Language: | English |
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Universiti Kebangsaan Malaysia
2014
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| Online Access: | http://journalarticle.ukm.my/8160/ http://journalarticle.ukm.my/8160/ http://journalarticle.ukm.my/8160/1/21_Nazrina.pdf |
| Summary: | There are two main reasons that motivate people to detect outliers; the first is the researchers’ intention; see the example of Mr Haldum’s cases in Barnett and Lewis. The second is the effect of outliers on analyses. This article does not differentiate between the various justifications for outlier detection. The aim was to advise the analyst about observations that are isolated from the other observations in the data set. In this article, we introduce the eigenstructure based angle for outlier detection. This method is simple and effective in dealing with masking and swamping problems. The method proposed is illustrated and compared with Mahalanobis distance by using several data sets. |
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