Independent component analysis via copula techniques

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

No 2008-004, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

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)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb649:sfb649dp2008-004

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