Detect outliers in multivariate datasets
Summary
This submission contains Matlab implementation of an iterative multivariate outlier detection algorithm
described in Hadi (1992) [1]. In addition to flagging potential outliers, the main function
DetectMultVarOutliers.m
also outputs robust estimates of the mean and covariance that it computes
during execution.
Deviating slightly from Hadi (1992), DetectMultVarOutliers.m
initializes the sample mean with the geometric median
of the dataset, instead of the coordinate-wise median. GeometricMedian.m
is the function used compute this
robust statistic; via the Weiszfeld's algorithm [2]. Note that this auxiliary function can be used on its own in
any application that requires robust estimation of central tendency of multivariate data corrupted by sampling
errors and/or noise.
For a quick demo on how to use the main function, see source code for outliers_demo.m
or simply enter outliers_demo
into Matlab command window.
References
[1] Hadi, A.S., 1992. Identifying multiple outliers in multivariate data. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 54(3), pp. 761-771.
[2] Weiszfeld, E., 1937. Sur le point par lequel la somme des distances den points donnés est minimum. Tohoku Mathematics Journal, Vol. 43, pp. 355–386.
License
MIT © 2019 Anton Semechko a.semechko@gmail.com
Zitieren als
Anton Semechko (2024). Detect outliers in multivariate datasets (https://github.com/AntonSemechko/Multivariate-Outliers), GitHub. Abgerufen .
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Version | Veröffentlicht | Versionshinweise | |
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1.0.1.5 | correct spelling error in the title |
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1.0.1.4 | Use README.md from GitHub |
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1.0.1.3 | Use README.md from GitHub |
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1.0.1.2 | - cosmetic description correction |
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1.0.1.1 | - modified submission description |
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1.0.1.0 | - migrated to GitHub |
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1.0.0.0 |
demo thumbnail
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