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• Robust second-order stationary spatial blind source separation using generalized sign matrices 

  Sipilä, Mika; Muehlmann, Christoph; Nordhausen, Klaus; Taskinen, Sara (Elsevier, 2024)

  Consider a spatial blind source separation model in which the observed multivariate spatial data are assumed to be a linear mixture of latent stationary spatially uncorrelated random fields. The objective is to recover an ...

• A more efficient second order blind identification method for separation of uncorrelated stationary time series 

  Taskinen, Sara; Miettinen, Jari; Nordhausen, Klaus (Elsevier BV, 2016)

  The classical second order source separation methods use approximate joint diagonalization of autocovariance matrices with several lags to estimate the unmixing matrix. Based on recent asymptotic results, we propose a novel ...

• Robustifying principal component analysis with spatial sign vectors 

  Taskinen, Sara; Koch, Inge; Oja, Hannu (Elsevier, 2012)

  In this paper, we apply orthogonally equivariant spatial sign covariance matrices as well as their affine equivariant counterparts in principal component analysis. The influence functions and asymptotic covariance matrices ...

• Affine-invariant rank tests for multivariate independence in independent component models 

  Oja, Hannu; Paindaveine, Davy; Taskinen, Sara (Institute of Mathematical Statistics, 2016)

  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 ...

• Test of the Latent Dimension of a Spatial Blind Source Separation Model 

  Muehlmann, Christoph; Bachoc, Francois; Nordhausen, Klaus; Yi, Mengxi (Institute of Statistical Science, Academia Sinica, 2024)

  We assume a spatial blind source separation model in which the observed multivariate spatial data is a linear mixture of latent spatially uncorrelated random fields containing a number of pure white noise components. We ...