A1 Journal article – refereed
Affine-invariant rank tests for multivariate independence in independent component models




List of Authors: Oja H, Paindaveine D, Taskinen S
Publisher: INST MATHEMATICAL STATISTICS
Publication year: 2016
Journal: Electronic Journal of Statistics
Journal name in source: ELECTRONIC JOURNAL OF STATISTICS
Journal acronym: ELECTRON J STAT
Volume number: 10
Issue number: 2
Number of pages: 48
ISSN: 1935-7524
eISSN: 1935-7524

Abstract
We consider the problem of testing for multivariate independence in independent component (IC) models. Under a symmetry assumption, we develop parametric and nonparametric (signed-rank) tests. Unlike in independent component analysis (ICA), we allow for the singular cases involving more than one Gaussian independent component. The proposed rank tests are based on componentwise signed ranks, a la Puri and Sen. Unlike the Puri and Sen tests, however, 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. Asymptotic local powers and asymptotic relative efficiencies with respect to Wilks' LRT are derived. Finite-sample properties are investigated through a Monte-Carlo study.


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Last updated on 2019-29-01 at 11:23