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Independent component analysis (ICA) is a statistical method for transforming multivariate data to components that are as independent of each other as possible. In recent years, several algorithms were proposed that perform well in many situations. But when the data contain outliers, these methods may lead to wrong conclusions. Here we robustify the well‐known FASTICA method by adding an outlier rejection rule, which does not assume elliptical symmetry. This modification is supported by simulations and real‐data examples.
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Brys et al. (2005) studied this question.
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