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dc.contributor.authorChen, Ray-Bingen
dc.contributor.authorGuo, Meihuien
dc.contributor.authorHärdle, Wolfgang Karlen
dc.contributor.authorHuang, Shih-Fengen
dc.date.accessioned2008-02-20-
dc.date.accessioned2009-07-23T15:03:29Z-
dc.date.available2009-07-23T15:03:29Z-
dc.date.issued2008-
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
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2008,004en
dc.subject.jelC01en
dc.subject.jelC13en
dc.subject.jelC14en
dc.subject.jelC63en
dc.subject.ddc330en
dc.subject.keywordBlind source separationen
dc.subject.keywordCanonical maximum likelihood methoden
dc.subject.keywordGivens rotation matrixen
dc.subject.keywordSignal/noise ratioen
dc.subject.keywordSimulated annealing algorithmen
dc.subject.stwFaktorenanalyseen
dc.subject.stwMaximum-Likelihood-Methodeen
dc.subject.stwHeuristisches Verfahrenen
dc.subject.stwTheorieen
dc.titleIndependent component analysis via copula techniques-
dc.type|aWorking Paperen
dc.identifier.ppn558748481en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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