Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/39288
Authors: 
Panov, Vladimir
Year of Publication: 
2010
Series/Report no.: 
SFB 649 discussion paper 2010,026
Abstract: 
In this article, we present new ideas concerning Non-Gaussian Component Analysis (NGCA). We use the structural assumption that a high-dimensional random vector X can be represented as a sum of two components - a lowdimensional signal S and a noise component N. We show that this assumption enables us for a special representation for the density function of X. Similar facts are proven in original papers about NGCA ([1], [5], [13]), but our representation differs from the previous versions. The new form helps us to provide a strong theoretical support for the algorithm; moreover, it gives some ideas about new approaches in multidimensional statistical analysis. In this paper, we establish important results for the NGCA procedure using the new representation, and show benefits of our method.
Subjects: 
dimension reduction
non-Gaussian components
EDR subspace
classification problem
Value at Risk
JEL: 
C13
C14
Document Type: 
Working Paper

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