Fast algorithm for identifying multivariate outliers in high-dimensional and/or large datasets, using spatial signs, see Filzmoser, Maronna, and Werner (CSDA, 2007). The computation of the distances is based on Mahalanobis distances.
Objects can be created by calls of the form
new("OutlierSign1", ...) but the
usual way of creating
OutlierSign1 objects is a call to the function
OutlierSign1() which serves as a constructor.
A list containing intermediate results of the SIGN1 algorithm for each class
Return the cutoff value used to identify outliers
Return a vector containing the computed distances
Valentin Todorov [email protected]
P. Filzmoser, R. Maronna and M. Werner (2008), Outlier identification in high dimensions, Computational Statistics & Data Analysis, Vol. 52 1694–1711.
P. Filzmoser & V. Todorov (2012), Robust tools for the imperfect world, To appear.
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