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.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 649 Discussion Paper ; No. 2010-050
- Klassifikation
-
Wirtschaft
Estimation: General
Semiparametric and Nonparametric Methods: General
- Thema
-
dimension reduction
non-Gaussian components
NGCA
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Panov, Vladimir
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (wo)
-
Berlin
- (wann)
-
2010
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:46 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Panov, Vladimir
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Entstanden
- 2010
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