A review on outliers-detection methods for multivariate data
Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and br...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Institute of Statistics Malaysia (ISMy)
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/31672/1/30586 http://umpir.ump.edu.my/id/eprint/31672/2/2021%20Abd%20Mutalib%20et%20al%20JOSMA.pdf http://umpir.ump.edu.my/id/eprint/31672/ https://doi.org/10.22452/josma.vol3no1.1 |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English English |
| Summary: | Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and brief outlier detection method for multivariate data which are projection pursuit method, methods based on robust distance and cluster analysis are reviewed. The strengths and weaknesses of each method are briefly discussed. |
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