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Sonderforschungsbereich 649: Ökonomisches Risiko, Humboldt-Universität Berlin >
SFB 649 Discussion Papers, HU Berlin >
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http://hdl.handle.net/10419/56723
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| Title: | | Estimation of the signal subspace without estimation of the inverse covariance matrix  |
| Authors: | | Panov, Vladimir |
| Issue Date: | | 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 |
| Appears in Collections: | | SFB 649 Discussion Papers, HU Berlin
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