Independent component analysis via copula techniques - Ray-Bing Chen; Meihui Guo; Wolfgang Karl Härdle; Shih-Feng Huang (Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, 2008)
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: C01; C13; C14; C63; 330; Blind source separation; Canonical maximum likelihood method; Givens rotation matrix; Signal/noise ratio; Simulated annealing algorithm; Faktorenanalyse; Maximum-Likelihood-Methode; Heuristisches Verfahren; Theorie