Arbeitspapier
Non-gaussian component analysis: New ideas, new proofs, new applications
In this article, we present new ideas concerning Non-Gaussian Component Analysis (NGCA). We use the structural assumption that a high-dimensional random vector X can be represented as a sum of two components - a lowdimensional signal S and a noise component N. We show that this assumption enables us for a special representation for the density function of X. Similar facts are proven in original papers about NGCA ([1], [5], [13]), but our representation differs from the previous versions. The new form helps us to provide a strong theoretical support for the algorithm; moreover, it gives some ideas about new approaches in multidimensional statistical analysis. In this paper, we establish important results for the NGCA procedure using the new representation, and show benefits of our method.
- Language
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Englisch
- Bibliographic citation
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Series: SFB 649 Discussion Paper ; No. 2010,026
- Classification
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Wirtschaft
Estimation: General
Semiparametric and Nonparametric Methods: General
- Subject
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dimension reduction
non-Gaussian components
EDR subspace
classification problem
Value at Risk
Hauptkomponentenanalyse
Statistische Verteilung
Clusteranalyse
Value at Risk
Theorie
- 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, 11:41 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Panov, Vladimir
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Time of origin
- 2010
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