Affine-invariant rank tests for multivariate independence in independent component models
Oja, H., Paindaveine, D., & Taskinen, S. (2016). Affine-invariant rank tests for multivariate independence in independent component models. Electronic Journal of Statistics, 10(2), 2372-2419. https://doi.org/10.1214/16-EJS1174
Julkaistu sarjassa
Electronic Journal of StatisticsPäivämäärä
2016Tekijänoikeudet
© the Authors, 2016. This is an open access article distributed under the terms of a Creative Commons License.
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.
Julkaisija
Institute of Mathematical StatisticsISSN Hae Julkaisufoorumista
1935-7524Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/26199510
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