Independent Component Analysis Via Copula Techniques

Chen, Ray-Bing, Meihui Guo, Wolfgang Karl Härdle and Huang, Shih-Feng

No SFB649DP2008-004, SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

Abstract: Independent component analysis (ICA) is a modern factor analysis tool de- veloped in the last two decades. Given p-dimensional data, we search for that linear combination of data which creates (almost) independent components. Here copulae are used to model the p-dimensional data and then independent components are found by optimizing the copula parameters. Based on this idea, we propose the COPICA method for searching independent components. We illustrate this method using several blind source separation examples, which are mathematically equivalent to ICA problems. Finally performances of our method and FastICA are compared to explore the advantages of this method.

Keywords: Blind source separation; Canonical maximum likelihood method; Givens rotation matrix; Signal/noise ratio; Simulated annealing algorithm (search for similar items in EconPapers)
JEL-codes: C01 C13 C14 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp and nep-ecm
Date: Written
View list of references View citations in EconPapers

Downloads: (external link)
http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2008-004.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:hum:wpaper:sfb649dp2008-004

Access Statistics for this paper

More papers in SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649
Contact information at EDIRC.
Series data maintained by Janine Tellinger ().