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Working paper

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

Economic risk. Discussion paper SFB 649. Humboldt-Universität zu Berlin, 2010. No. 2010-050.
Let a high-dimensional random vector $\vX$ be represented as a sum of two components - a  signal $\vS$ that belongs to some low-dimensional linear subspace $\S$,  and a noise component $\vN$.  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 appear in similar methods - it requires neither the estimation of the inverse covariance  matrix of $\vX$ nor the estimation of the covariance matrix of $\vN$.