Independent Component Analysis

In the independent component (IC) model it is assumed that the p-variate random vector x = Ωz + μ, where μ is a location vector, Ω is a full rank p× p mixing matrix, and z is a p-variate vector with mutually independent components. In the independent component analysis (ICA) the aim is to find an estimate of an unmixing matrix Γ such that Γx has independent components. We talk about standardization of the IC model, and on the basis of n independent copies of x, we consider one-sample testing and estimation procedures for Ω (or Γ). We also discuss comparison of different unmixing matrix estimates.

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