Statistics and Computing

, Volume 25, Issue 2, pp 273–288

COPICA—independent component analysis via copula techniques

  • Ray-Bing Chen
  • Meihui Guo
  • Wolfgang K. Härdle
  • Shih-Feng Huang

DOI: 10.1007/s11222-013-9431-3

Cite this article as:
Chen, RB., Guo, M., Härdle, W.K. et al. Stat Comput (2015) 25: 273. doi:10.1007/s11222-013-9431-3


Independent component analysis (ICA) is a modern computational method developed in the last two decades. The main goal of ICA is to recover the original independent variables by linear transformations of the observations. In this study, a copula-based method, called COPICA, is proposed to solve the ICA problem. The proposed COPICA method is a semiparametric approach, the marginals are estimated by nonparametric empirical distributions and the joint distributions are modeled by parametric copula functions. The COPICA method utilizes the estimated copula parameter as a dependence measure to search the optimal rotation matrix that achieves the ICA goal. Both simulation and empirical studies are performed to compare the COPICA method with the state-of-art methods of ICA. The results indicate that the COPICA attains higher signal-to-noise ratio (SNR) than several other ICA methods in recovering signals. In particular, the COPICA usually leads to higher SNRs than FastICA for near-Gaussian-tailed sources and is competitive with a nonparametric ICA method for two dimensional sources. For higher dimensional ICA problem, the advantage of using the COPICA is its less storage and less computational effort.


Blind source separation Canonical maximum likelihood method Givens rotation matrix Signal/noise ratio Simulated annealing algorithm 

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ray-Bing Chen
    • 1
  • Meihui Guo
    • 2
  • Wolfgang K. Härdle
    • 3
    • 4
  • Shih-Feng Huang
    • 5
  1. 1.Dept. of StatisticsNational Cheng Kung UniversityTainanTaiwan
  2. 2.Dept. of Applied Math.National Sun Yat-sen UniversityKaohsiungTaiwan
  3. 3.Center for Applied Statistics and EconomicsHumboldt-Universität zu BerlinBerlinGermany
  4. 4.Lee Kong Chian School of BusinessSingapore Management UniversitySingaporeSingapore
  5. 5.Dept. of Applied Math.National University of KaohsiungKaohsiungTaiwan

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