Parametric and Nonparametric Test for Multivariate Independence in IC Models
The so-called independent component (IC) model states that the observed p-vector X is generated via X = Z + ?, where ? is a p-vector, is an invertible matrix, and the centered random vector Z has independent marginals Zi. We consider the problem of testing, on the basis of n i.i.d. copies of X = (X(1)?,X(2)?)?, the null hypothesis under which the multivariate marginals X(1) and X(2) are independent. Under a symmetry assumption on the Zi’s, we propose parametric and nonparametric tests based on estimated independent components (which are obtained under the null, via, e.g., a recent estimator due to Oja et al. 2006). Far from excluding cases of unidentifiability where several independent components are Gaussian, as it is done in the so-called independent component analysis (ICA), our procedures can deal with the resulting possible model singularity, the nature of which we carefully investigate. The proposed nonparametric tests are based on componentwise signed ranks, in the same spirit as in Puri and Sen (1971). However, unlike the Puri and Sen tests, our tests (i) are affine-invariant and (ii) are, for adequately chosen scores, locally and asymptotically optimal (in the Le Cam sense) at prespecified densities. They are also valid without any moment assumptions. Local powers and asymptotic relative efficiencies with respect to the classical Gaussian procedure (namely, Wilks’ LRT) are derived. Finite-sample properties are investigated through a Monte-Carlo study.
Tests of independence, signed rank tests, independent component models, local asymptotic normality, singular information matrices.
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