Efficient independent component analysis
arXiv:0705.4230 · doi:10.1214/009053606000000939
Abstract
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.
Published at http://dx.doi.org/10.1214/009053606000000939 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Cited by in corpus (6)
- Fourth Moments and Independent Component Analysis
- Separation of uncorrelated stationary time series using autocovariance matrices
- Semiparametrically efficient inference based on signed ranks in symmetric independent component models
- Quantifying identifiability in independent component analysis
- Boosting Independent Component Analysis
- Second-order Approximation of Minimum Discrimination Information in Independent Component Analysis