摘要： 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. 其他題名： Stat Comput 出版者： Boston: Springer US 出版日期： 2015-03-01 出處： Statistics and computing, 2015-03, Vol.25 (2), p.273-288 資源來源： SpringerLink Journals - AutoHoldings 版權： Springer Science+Business Media New York 2014 識別號： ISSN: 0960-3174 識別號： EISSN: 1573-1375 識別號： DOI: 10.1007/s11222-013-9431-3
