Arbeitspapier
Estimation of the signal subspace without estimation of the inverse covariance matrix
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.
- Language
-
Englisch
- Bibliographic citation
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Series: SFB 649 Discussion Paper ; No. 2010-050
- Classification
-
Wirtschaft
Estimation: General
Semiparametric and Nonparametric Methods: General
- Subject
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dimension reduction
non-Gaussian components
NGCA
- Event
-
Geistige Schöpfung
- (who)
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Panov, Vladimir
- Event
-
Veröffentlichung
- (who)
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Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (where)
-
Berlin
- (when)
-
2010
- Handle
- Last update
-
10.03.2025, 11:46 AM CET
Data provider
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Object type
- Arbeitspapier
Associated
- Panov, Vladimir
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Time of origin
- 2010
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