Significance in the Forward Search
نویسندگان
چکیده
Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In recent years, many algorithms were proposed that perform very well in many situations. Nevertheless, when outlying values are present in the data, these methods often lead to wrong conclusions. Therefore, we adapted here the well known FASTICA method by adding an outlier rejection rule for non-normal data. To conclude, we give some real data examples. Approximating Long Range Financial Data: An Example of Model Choice Laurie Davies Department of Mathematics, University of Duisburg-Essen, Germany
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تاریخ انتشار 2005