Independent component analysis (ICA) has been widely used for blind source separation in many fields, such as brain imaging analysis, signal processing and telecommunication. Many statistical techniques based on M-estimates have been proposed for estimating the mixing matrix. Recently, several nonparametric methods have been developed, but in-depth analysis of asymptotic efficiency has not been available. We analyze ICA using semiparametric theories and propose a straightforward estimate based on the efficient score function by using B-spline approximations. The estimate is asymptotically efficient under moderate conditions and exhibits better performance than standard ICA methods in a variety of simulations.
@article{1179935066,
author = {Chen, Aiyou and Bickel, Peter J.},
title = {Efficient independent component analysis},
journal = {Ann. Statist.},
volume = {34},
number = {1},
year = {2006},
pages = { 2825-2855},
language = {en},
url = {http://dml.mathdoc.fr/item/1179935066}
}
Chen, Aiyou; Bickel, Peter J. Efficient independent component analysis. Ann. Statist., Tome 34 (2006) no. 1, pp. 2825-2855. http://gdmltest.u-ga.fr/item/1179935066/