Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25246
Authors: 
Chen, Ray-Bing
Guo, Meihui
Härdle, Wolfgang Karl
Huang, Shih-Feng
Year of Publication: 
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

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.