Efficient independent component analysis

• 成果类型: Article; Proceedings Paper
• 署名作者: Chen, Aiyou; Bickel, Peter J.
• 署名单位: Alcatel-Lucent; Lucent Technologies; AT&T; University of California System; University of California Berkeley
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053606000000939
• 发表日期: 2006
• 页码: 2825-2855
• 关键词: separation; likelihood

摘要

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.