Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/25246 
Erscheinungsjahr: 
2008
Schriftenreihe/Nr.: 
SFB 649 Discussion Paper No. 2008,004
Verlag: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
Zusammenfassung: 
Independent component analysis (ICA) is a modern factor analysis tool developed 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.
Schlagwörter: 
Blind source separation
Canonical maximum likelihood method
Givens rotation matrix
Signal/noise ratio
Simulated annealing algorithm
JEL: 
C01
C13
C14
C63
Dokumentart: 
Working Paper

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