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dc.contributor.authorChen, Ray-Bingen_US
dc.contributor.authorGuo, Meihuien_US
dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorHuang, Shih-Fengen_US
dc.date.accessioned2008-02-20en_US
dc.date.accessioned2009-07-23T15:03:29Z-
dc.date.available2009-07-23T15:03:29Z-
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10419/25246-
dc.description.abstractIndependent 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.en_US
dc.language.isoengen_US
dc.publisherSFB 649, Economic Risk Berlinen_US
dc.relation.ispartofseriesSFB 649 discussion paper 2008,004en_US
dc.subject.jelC01en_US
dc.subject.jelC13en_US
dc.subject.jelC14en_US
dc.subject.jelC63en_US
dc.subject.ddc330en_US
dc.subject.keywordBlind source separationen_US
dc.subject.keywordCanonical maximum likelihood methoden_US
dc.subject.keywordGivens rotation matrixen_US
dc.subject.keywordSignal/noise ratioen_US
dc.subject.keywordSimulated annealing algorithmen_US
dc.subject.stwFaktorenanalyseen_US
dc.subject.stwMaximum-Likelihood-Methodeen_US
dc.subject.stwHeuristisches Verfahrenen_US
dc.subject.stwTheorieen_US
dc.titleIndependent component analysis via copula techniquesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn558748481en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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