Skip to content
Compute robust estimates of mean and covariance, and flag potential multivariate outliers
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.


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.


[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.


MIT © 2019 Anton Semechko

You can’t perform that action at this time.