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Title:Independent component analysis via copula techniques PDF Logo
Authors:Chen, Ray-Bing
Guo, Meihui
Härdle, Wolfgang Karl
Huang, Shih-Feng
Issue Date:2008
Series/Report no.:SFB 649 discussion paper 2008,004
Abstract: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.
Subjects:Blind source separation
Canonical maximum likelihood method
Givens rotation matrix
Signal/noise ratio
Simulated annealing algorithm
JEL:C01
C13
C14
C63
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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