Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Statistics > Methodology

arXiv:0705.4230 (stat)
[Submitted on 29 May 2007 (v1), last revised 2 Aug 2007 (this version, v2)]

Title:Efficient independent component analysis

Authors:Aiyou Chen, Peter J. Bickel
View PDF
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.
Comments: Published at this http URL in the Annals of Statistics (this http URL) by the Institute of Mathematical Statistics (this http URL)
Subjects: Methodology (stat.ME); Statistics Theory (math.ST); Machine Learning (stat.ML)
MSC classes: 62G05 (Primary) 62H12 (Secondary)
Report number: IMS-AOS-AOS0205
Cite as: arXiv:0705.4230 [stat.ME]
  (or arXiv:0705.4230v2 [stat.ME] for this version)
  https://doi.org/10.48550/arXiv.0705.4230
Journal reference: Annals of Statistics 2006, Vol. 34, No. 6, 2825-2855
Related DOI: https://doi.org/10.1214/009053606000000939

Submission history

From: Aiyou Chen [view email]
[v1] Tue, 29 May 2007 15:15:33 UTC (65 KB)
[v2] Thu, 2 Aug 2007 06:04:57 UTC (170 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
view license
Current browse context:
stat.ME
< prev   |   next >
new | recent | 2007-05
Change to browse by:
math
math.ST
stat
stat.ML
stat.TH

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)