Optimal Sampling Strategies for Multiscale Models with Application to Network Traffic Estimation

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE Workshop on Statistical Signal Processing (SSP)en_US
dc.citation.locationSt. Louis, MOen_US
dc.contributor.authorRibeiro, Vinay Josephen_US
dc.contributor.authorRiedi, Rudolf H.en_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T01:00:32Zen_US
dc.date.available2007-10-31T01:00:32Zen_US
dc.date.issued2003-10-01en_US
dc.date.modified2006-06-21en_US
dc.date.note2004-02-25en_US
dc.date.submitted2003-10-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractThis paper considers the problem of determining which set of 2<sup><i>p</i></sup> leaf nodes on a binary multiscale tree model of depth N (<i>N</i>><i>p</i>) gives the best linear minimum mean-squared estimator of the tree root. We find that the best-case and worst-case sampling choices depend on the correlation structure of the tree. This problem arises in Internet traffic estimation, where the goal is to estimate the average traffic rate on a network path based on a limited number of traffic samples.en_US
dc.identifier.citationV. J. Ribeiro, R. H. Riedi and R. G. Baraniuk, "Optimal Sampling Strategies for Multiscale Models with Application to Network Traffic Estimation," 2003.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/SSP.2003.1289360en_US
dc.identifier.urihttps://hdl.handle.net/1911/20253en_US
dc.language.isoengen_US
dc.relation.projecthttp://spin.rice.eduen_US
dc.relation.softwarehttp://spin.rice.edu/Softwareen_US
dc.subjecttree multiscale optimal sampling networks traffic estimationen_US
dc.subject.keywordtree multiscale optimal sampling networks traffic estimationen_US
dc.subject.otherSignal Processing for Networkingen_US
dc.titleOptimal Sampling Strategies for Multiscale Models with Application to Network Traffic Estimationen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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