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
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisenssi
Ellei muuten mainita, aineiston lisenssi on © the Authors, 2016. This is an open access article distributed under the terms of a Creative Commons License.
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Sign and rank covariance matrices with applications to multivariate analysis
Ollila, Esa (University of Jyväskylä, 2002) -
Examining stability of independent component analysis based on coefficient and component matrices for voxel-based morphometry of structural magnetic resonance imaging
Zhang, Qing; Hu, Guoqiang; Tian, Lili; Ristaniemi, Tapani; Wang, Huili; Chen, Hongjun; Wu, Jianlin; Cong, Fengyu (Springer Netherlands, 2018)Independent component analysis (ICA) on group-level voxel-based morphometry (VBM) produces the coefficient matrix and the component matrix. The former contains variability among multiple subjects for further statistical ... -
Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition
Hu, Guoqiang; Zhang, Qing; Waters, Abigail B.; Li, Huanjie; Zhang, Chi; Wu, Jianlin; Cong, Fengyu; Nickerson, Lisa D. (Elsevier BV, 2019)Background. Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) ... -
Extracting conditionally heteroskedastic components using independent component analysis
Miettinen, Jari; Matilainen, Markus; Nordhausen, Klaus; Taskinen, Sara (Wiley-Blackwell, 2020)In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In ... -
Independent component analysis based on symmetrised scatter matrices
Taskinen, Sara; Sirkiä, Seija; Oja, Hannu (Elsevier, 2007)A new method for separating the mixtures of independent sources has been proposed recently in [Oja et al. (2006). Scatter matrices and independent component analysis. Austrian J. Statist., to appear]. This method is based ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.