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dc.contributor.authorSipilä, Mika
dc.contributor.authorMuehlmann, Christoph
dc.contributor.authorNordhausen, Klaus
dc.contributor.authorTaskinen, Sara
dc.date.accessioned2023-12-20T10:18:07Z
dc.date.available2023-12-20T10:18:07Z
dc.date.issued2024
dc.identifier.citationSipilä, M., Muehlmann, C., Nordhausen, K., & Taskinen, S. (2024). Robust second-order stationary spatial blind source separation using generalized sign matrices. <i>Spatial Statistics</i>, <i>59</i>, Article 100803. <a href="https://doi.org/10.1016/j.spasta.2023.100803" target="_blank">https://doi.org/10.1016/j.spasta.2023.100803</a>
dc.identifier.otherCONVID_197360950
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/92425
dc.description.abstractConsider 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 unknown mixing procedure as well as the latent random fields. Recently, spatial blind source separation methods that are based on the simultaneous diagonalization of two or more scatter matrices were proposed. In cases involving uncontaminated data, such methods can solve the blind source separation problem, however, in the presence of outlying observations, these methods perform poorly. We propose a robust blind source separation method that employs robust global and local covariance matrices based on generalized spatial signs in simultaneous diagonalization. Simulation studies are employed to illustrate the robustness and efficiency of the proposed methods in various scenarios.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesSpatial Statistics
dc.rightsCC BY 4.0
dc.subject.otheraffine equivariance
dc.subject.otherbias
dc.subject.othermultivariate spatial data
dc.subject.otherscatter matrix
dc.subject.otherspatial signs
dc.titleRobust second-order stationary spatial blind source separation using generalized sign matrices
dc.typeresearch article
dc.identifier.urnURN:NBN:fi:jyu-202312208419
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn2211-6753
dc.relation.volume59
dc.type.versionpublishedVersion
dc.rights.copyright© 2023 The Author(s). Published by Elsevier B.V.
dc.rights.accesslevelopenAccessfi
dc.type.publicationarticle
dc.subject.ysopaikkatietoanalyysi
dc.subject.ysosignaalinkäsittely
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p28516
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.spasta.2023.100803
jyx.fundinginformationThe work of CM and KN was supported by the Austrian Science Fund P31881-N32. The work of KN and ST was supported by the COST Action HiTEc (CA21163). The work of MS was supported by the Vilho, Yrjö and Kalle Väisälä Fund, Finland.
dc.type.okmA1


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