Sign and rank covariance matrices with applications to multivariate analysis
PublisherUniversity of Jyväskylä
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- Väitöskirjat 
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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 ...
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 ...
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 ...
An Investigation of the Robustness in the Travelling Salesman Problem Routes Using Special Structured Matrices Aziz, Azmin Azliza; Mousavi Abdehgah, Mohsen; Tavana, Madjid; Niaki, Seyed Taghi Akhavan (Taylor & Francis, 2020)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 ...
Sirkiä, Seija (University of Jyväskylä, 2007)