Näytä suppeat kuvailutiedot

dc.contributor.authorMiettinen, Jari
dc.contributor.authorNordhausen, Klaus
dc.contributor.authorTaskinen, Sara
dc.date.accessioned2017-01-23T08:51:44Z
dc.date.available2017-01-23T08:51:44Z
dc.date.issued2017
dc.identifier.citationMiettinen, J., Nordhausen, K., & Taskinen, S. (2017). Blind Source Separation Based on Joint Diagonalization in R : The Packages JADE and BSSasymp. <i>Journal of Statistical Software</i>, <i>76</i>(2), 1-31. <a href="https://doi.org/10.18637/jss.v076.i02" target="_blank">https://doi.org/10.18637/jss.v076.i02</a>
dc.identifier.otherCONVID_26487836
dc.identifier.otherTUTKAID_72661
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/52792
dc.description.abstractBlind source separation (BSS) is a well-known signal processing tool which is used to solve practical data analysis problems in various fields of science. In BSS, we assume that the observed data consists of linear mixtures of latent variables. The mixing system and the distributions of the latent variables are unknown. The aim is to find an estimate of an unmixing matrix which then transforms the observed data back to latent sources. In this paper we present the R packages JADE and BSSasymp. The package JADE offers several BSS methods which are based on joint diagonalization. Package BSSasymp contains functions for computing the asymptotic covariance matrices as well as their data-based estimates for most of the BSS estimators included in package JADE. Several simulated and real datasets are used to illustrate the functions in these two packages.
dc.language.isoeng
dc.publisherFoundation for Open Access Statistics
dc.relation.ispartofseriesJournal of Statistical Software
dc.subject.othermultivariate time series
dc.subject.othernonstationary source separation
dc.subject.otherperformance indices
dc.subject.othersecond order source separation
dc.subject.otherstatistics
dc.titleBlind Source Separation Based on Joint Diagonalization in R : The Packages JADE and BSSasymp
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201701171174
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2017-01-17T16:15:07Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1-31
dc.relation.issn1548-7660
dc.relation.numberinseries2
dc.relation.volume76
dc.type.versionpublishedVersion
dc.rights.copyright© the Authors, 2017. This is an open access article under the terms of the Creative Commons Attribution 3.0 Unported License.
dc.rights.accesslevelopenAccessfi
dc.subject.ysomatematiikka
dc.subject.ysoriippumattomien komponenttien analyysi
jyx.subject.urihttp://www.yso.fi/onto/yso/p3160
jyx.subject.urihttp://www.yso.fi/onto/yso/p38529
dc.rights.urlhttps://creativecommons.org/licenses/by/3.0/
dc.relation.doi10.18637/jss.v076.i02
dc.type.okmA1


Aineistoon kuuluvat tiedostot

Thumbnail

Aineisto kuuluu seuraaviin kokoelmiin

Näytä suppeat kuvailutiedot

© the Authors, 2017. This is an open access article under the terms of the Creative Commons Attribution 3.0 Unported License.
Ellei muuten mainita, aineiston lisenssi on © the Authors, 2017. This is an open access article under the terms of the Creative Commons Attribution 3.0 Unported License.