Estimation of the signal subspace without estimation of the inverse covariance matrix

Vladimir Panov

No 2010-050, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: dimension reduction; non-Gaussian components; NGCA (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2010
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