Abstract Details
Activity Number:
|
228
|
Type:
|
Contributed
|
Date/Time:
|
Monday, August 4, 2014 : 2:00 PM to 3:50 PM
|
Sponsor:
|
IMS
|
Abstract #313683
|
View Presentation
|
Title:
|
R-Estimation for Asymmetric Independent Component Analysis
|
Author(s):
|
Chintan Mehta*+
|
Companies:
|
Yale University
|
Keywords:
|
ranks ;
independent component analysis (ICA) ;
local asymptotic normality (LAN) ;
R-estimation ;
robustness
|
Abstract:
|
Independent Component Analysis (ICA) recently has attracted attention in the statistical literature as an alternative to elliptical models. We focus here on estimating the model's mixing matrix. Traditional methods (FOBI, Kernel-ICA, FastICA) originating from the engineering literature have consistency that requires moment conditions without achieving any type of asymptotic efficiency. Those estimators with favorble robustness features tend to have unclear optimality properties. An efficient (signed-)rank-based approach has been proposed by Ilmonen and Paindaveine (2011) for the case of symmetric component densities that fail to be root-n consistent as soon as a single component density is asymmetric. In this paper, using ranks rather than signed ranks, we extend their approach to the asymmetric case and propose a one-step R-estimator for ICA mixing matrices. Finally, we show, through finite-sample experiments and by an empirical exercise, that our methods also may provide excellent results in a context such as image analysis, where the basic assumptions of ICA are quite unlikely to hold.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.