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 ...
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 ...
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 ...
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 ...
In this study, the robustness of the Travelling Salesman Problem (TSP) routes is investigated by recognising the special combinatorial structures of Kalmanson matrices. A recognition algorithm encompassing three procedures ...
