Covariate-Adjusted Independent Component Analysis
Date Issued
2004
Date
2004
Author(s)
Pan, Jia-Chiun
DOI
en-US
Abstract
Independent component analysis (ICA) is a recently developed statistical and computational technique for discovering mutually independent nongaussian latent variables from observed multivariate data in the fields of neural networks and signal processing. It can potentially be applied to many application fields such as brain imaging, audio separation, telecommunication, feature extraction, economics, psychology, physiology, biomedical engineering, and bioinformatics, whenever the assumptions of statistical independence and nongaussianity are substantively justifiable. In current practice, one applies the standard procedure(s) of ICA directly to the observed multivariate variables, even though they may be affected by some known covariates, to identify the independent components and estimate the mixing coefficients. In this study, we find that ignoring those relevant covariates may lead to a biased result of ICA, and then suggest a covariate-adjusted ICA to minimize such biases by applying the standard procedure(s) of ICA to the residuals from the regressions of the observed multivariate variables on those relevant covariates in a linear ICA model. A simulation study is conducted using the FastICA algorithm to examine the statistical properties of our covariate-adjusted ICA and to derive numerically the sampling distributions of the estimated mixing coefficients as an interesting by-product. Finally, two examples are given for illustration.
Subjects
變數調整
獨立成份
調整變數效應
ICA
independent component analysis
adjusted
covariate-adjusted
covariates
Type
thesis
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