Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56723
Authors: 
Panov, Vladimir
Year of Publication: 
2010
Series/Report no.: 
SFB 649 discussion paper 2010-050
Abstract: 
Let a high-dimensional random vector X can be represented as a sum of two components - a signal S , which belongs to some low-dimensional subspace S, and a noise component N . This paper presents a new approach for estimating the subspace S based on the ideas of the Non-Gaussian Component Analysis. Our approach avoids the technical difficulties that usually exist in similar methods - it doesn't require neither the estimation of the inverse covariance matrix of X nor the estimation of the covariance matrix of N.
Subjects: 
dimension reduction
non-Gaussian components
NGCA
JEL: 
C13
C14
Document Type: 
Working Paper

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