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dc.contributor.authorLópez Valcarce, Roberto
dc.contributor.authorRomero Gonzalez, Daniel
dc.contributor.authorSala Álvarez, José
dc.contributor.authorPagès Zamora, Alba Maria
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2016-11-29T13:31:08Z
dc.date.issued2016
dc.identifier.citationLópez, R., Romero, D., Sala, J., Pages, A. Distributed multivariate regression with unknown noise covariance in the presence of outliers: an MDL approach. A: IEEE Statistical Signal Processing Workshop. "2016 IEEE Statistical Signal Processing Workshop (SSP) took place 25-29 June 2016 in Palma de Mallorca, Spain". Palma de Mallorca: Institute of Electrical and Electronics Engineers (IEEE), 2016.
dc.identifier.isbn978-1-4673-7802-4
dc.identifier.urihttp://hdl.handle.net/2117/97423
dc.description.abstractWe consider the problem of estimating the coefficients in a multivariable linear model by means of a wireless sensor network which may be affected by anomalous measurements. The noise covariance matrices at the different sensors are assumed unknown. Treating outlying samples, and their support, as additional nuisance parameters, the Maximum Likelihood estimate is investigated, with the number of outliers being estimated according to the Minimum Description Length principle. A distributed implementation based on iterative consensus techniques is then proposed, and it is shown effective for managing outliers in the data.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
dc.subject.lcshSignal processing
dc.subject.lcshWireless LANs
dc.subject.otherCovariance matrices
dc.subject.otherIterative methods
dc.subject.otherMaximum likelihood estimation
dc.subject.otherRegression analysis
dc.subject.otherWireless sensor networks
dc.subject.otherDistributed multivariate regression
dc.subject.otherUnknown noise covariance matrices
dc.subject.otherMDL approach
dc.subject.otherWireless sensor network
dc.subject.otherMaximum likelihood estimate
dc.subject.otherMinimum description length principle
dc.subject.otherIterative consensus techniques
dc.titleDistributed multivariate regression with unknown noise covariance in the presence of outliers: an MDL approach
dc.typeConference report
dc.subject.lemacTractament del senyal
dc.subject.lemacXarxes locals sense fil Wi-Fi
dc.contributor.groupUniversitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
dc.identifier.doi10.1109/SSP.2016.7551769
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7551769/
dc.rights.accessOpen Access
drac.iddocument18963973
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2013-41315-R
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2015-69648
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2013-41315-R
dc.date.lift10000-01-01
upcommons.citation.authorLópez, R., Romero, D., Sala, J., Pages, A.
upcommons.citation.contributorIEEE Statistical Signal Processing Workshop
upcommons.citation.pubplacePalma de Mallorca
upcommons.citation.publishedtrue
upcommons.citation.publicationName2016 IEEE Statistical Signal Processing Workshop (SSP) took place 25-29 June 2016 in Palma de Mallorca, Spain


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