Abstract
We consider the problem of nonparametric estimation of a d-dimensional probability density and its `principal directions' in the independent component analysis model. A new method of estimation based on diagonalization of nonparametric estimates of certain matrix functionals of the density is suggested. We show that the proposed estimators of principal directions are -consistent and that the corresponding density estimators converge at the optimal rate.
Citation
Alexander Samarov. Alexandre Tsybakov. "Nonparametric independent component analysis." Bernoulli 10 (4) 565 - 582, August 2004. https://doi.org/10.3150/bj/1093265630
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