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dc.contributor.authorRadojičić, Una
dc.contributor.authorNordhausen, Klaus
dc.contributor.editorAnsari, Jonathan
dc.contributor.editorFuchs, Sebastian
dc.contributor.editorTrutschnig, Wolfgang
dc.contributor.editorAsunción Lubiano, María
dc.contributor.editorÁngeles Gil, María
dc.contributor.editorGrzegorzewski, Przemyslaw
dc.contributor.editorHryniewicz, Olgierd
dc.date.accessioned2024-08-21T09:25:51Z
dc.date.available2024-08-21T09:25:51Z
dc.date.issued2024
dc.identifier.citationRadojičić, U., & Nordhausen, K. (2024). Order Determination in Second-Order Source Separation Models Using Data Augmentation. In J. Ansari, S. Fuchs, W. Trutschnig, M. Asunción Lubiano, M. Ángeles Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.), <i>Combining, Modelling and Analyzing Imprecision, Randomness and Dependence</i> (pp. 371-379). Springer. Advances in Intelligent Systems and Computing, 1458. <a href="https://doi.org/10.1007/978-3-031-65993-5_46" target="_blank">https://doi.org/10.1007/978-3-031-65993-5_46</a>
dc.identifier.otherCONVID_233411155
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/96709
dc.description.abstractWe propose a robust estimator for the number of latent components in an internal noise model within the second-order source separation (SOS) framework. Our approach utilizes a data augmentation strategy in conjunction with the robust SOS approach eSAM-AMUSE, which combines information from eigenvalues and variations of eigenvectors of eSAM-AMUSE. The resulting dimension estimate can be visualized using a ladle plot. Through a simulation study, we demonstrate the superior properties of the new estimator, which outperforms the bootstrap-based AMUSEladle estimator.en
dc.format.extent565
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofCombining, Modelling and Analyzing Imprecision, Randomness and Dependence
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computing
dc.rightsIn Copyright
dc.titleOrder Determination in Second-Order Source Separation Models Using Data Augmentation
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202408215602
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-3-031-65992-8
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange371-379
dc.relation.issn2194-5357
dc.type.versionacceptedVersion
dc.rights.copyright© 2024 the Authors
dc.rights.accesslevelembargoedAccessfi
dc.relation.conferenceInternational Conference on Soft Methods in Probability and Statistics
dc.subject.ysomallintaminen
dc.subject.ysotilastomenetelmät
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p3533
jyx.subject.urihttp://www.yso.fi/onto/yso/p3127
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-031-65993-5_46
jyx.fundinginformationThe work of Una Radojičić is supported by the Austrian Science Foundation, project number I 5799-N.
dc.type.okmA4


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