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 [email protected]