Updated 08 May 2019
This submission contains Matlab implementation of an iterative multivariate outlier detection algorithm described in Hadi (1992) . 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 . 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.
 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.
 Geometric median: http://en.wikipedia.org/wiki/Geometric_median
 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.
Anton Semechko (2019). Detect outliers in multivaraite datasets (https://www.github.com/AntonSemechko/Multivariate-Outliers), GitHub. Retrieved .
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