|
EconStor >
Humboldt-Universität Berlin >
Sonderforschungsbereich 649: Ökonomisches Risiko, Humboldt-Universität Berlin >
SFB 649 Discussion Papers, HU Berlin >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/25246
|
| | |
| Title: | | Independent component analysis via copula techniques  |
| Authors: | | Chen, Ray-Bing Guo, Meihui Härdle, Wolfgang Karl Huang, Shih-Feng |
| Issue Date: | | 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 |
| Appears in Collections: | | SFB 649 Discussion Papers, HU Berlin
|
| Files in This Item:
| |
|
| No. of Downloads:
| |
| last Month |
last 3 Month |
total |
|
|
|
|
|
| |
| | |
Download bibliographical data as:
BibTeX
|
| |
Share on:http://hdl.handle.net/10419/25246
|
Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.
|